diff --git a/src/finn/custom_op/fpgadataflow/addstreams_batch.py b/src/finn/custom_op/fpgadataflow/addstreams_batch.py
index 856f84fae0cb90abcc08fc098d684872b1a6a6a1..aaf8687de1ace42c20bd55362850707bfeddcd88 100644
--- a/src/finn/custom_op/fpgadataflow/addstreams_batch.py
+++ b/src/finn/custom_op/fpgadataflow/addstreams_batch.py
@@ -29,7 +29,7 @@
import numpy as np
import os
import warnings
-from onnx import TensorProto, helper
+from onnx import helper
from finn.core.datatype import DataType
from finn.custom_op.fpgadataflow.hlscustomop import HLSCustomOp
@@ -84,18 +84,11 @@ 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",
+ "RandomNormal",
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),
- ),
+ shape=list(oshape),
)
def infer_node_datatype(self, model):
diff --git a/src/finn/custom_op/fpgadataflow/channelwise_op_batch.py b/src/finn/custom_op/fpgadataflow/channelwise_op_batch.py
index 073d6620ac3c2a4f62ac544e74ecf21b6e36d58f..b274bb8dc195cc9a0e84baa4a232bc73186ae1c4 100644
--- a/src/finn/custom_op/fpgadataflow/channelwise_op_batch.py
+++ b/src/finn/custom_op/fpgadataflow/channelwise_op_batch.py
@@ -30,7 +30,7 @@ import numpy as np
import os
import warnings
from math import ceil
-from onnx import TensorProto, helper
+from onnx import helper
from finn.core.datatype import DataType
from finn.custom_op.fpgadataflow.hlscustomop import HLSCustomOp
@@ -125,17 +125,11 @@ 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",
+ "RandomNormal",
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),
- ),
+ shape=list(oshape),
)
def infer_node_datatype(self, model):
diff --git a/src/finn/custom_op/fpgadataflow/convolutioninputgenerator.py b/src/finn/custom_op/fpgadataflow/convolutioninputgenerator.py
index 9ec7bc662d95b1c94ca17bc3c9a1a7b6199cc18a..8566ee035d058904ac7203b9180ac19b4a458025 100644
--- a/src/finn/custom_op/fpgadataflow/convolutioninputgenerator.py
+++ b/src/finn/custom_op/fpgadataflow/convolutioninputgenerator.py
@@ -29,7 +29,7 @@
import math
import numpy as np
import os
-from onnx import TensorProto, helper
+from onnx import helper
from finn.core.datatype import DataType
from finn.custom_op.fpgadataflow.hlscustomop import HLSCustomOp
@@ -148,17 +148,11 @@ 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",
+ "RandomNormal",
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),
- ),
+ shape=list(oshape),
)
def infer_node_datatype(self, model):
diff --git a/src/finn/custom_op/fpgadataflow/convolutioninputgenerator1d.py b/src/finn/custom_op/fpgadataflow/convolutioninputgenerator1d.py
index b428210acfd70186f68e7f1b35cfcd945d0a77d9..c76cb47d2f8f1374777b8fb58729057c1e596e2b 100644
--- a/src/finn/custom_op/fpgadataflow/convolutioninputgenerator1d.py
+++ b/src/finn/custom_op/fpgadataflow/convolutioninputgenerator1d.py
@@ -29,7 +29,7 @@
import math
import numpy as np
import os
-from onnx import TensorProto, helper
+from onnx import helper
from finn.core.datatype import DataType
from finn.custom_op.fpgadataflow.hlscustomop import HLSCustomOp
@@ -137,18 +137,11 @@ 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",
+ "RandomNormal",
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),
- ),
+ shape=list(oshape),
)
def infer_node_datatype(self, model):
diff --git a/src/finn/custom_op/fpgadataflow/downsampler.py b/src/finn/custom_op/fpgadataflow/downsampler.py
index 2313ab28b41668b93a55298aa2b589dac999070e..de251a84861d1dc6a120a9ec1ef7e620bc2f03b2 100644
--- a/src/finn/custom_op/fpgadataflow/downsampler.py
+++ b/src/finn/custom_op/fpgadataflow/downsampler.py
@@ -1,7 +1,7 @@
import numpy as np
import os
import warnings
-from onnx import TensorProto, helper
+from onnx import helper
from finn.core.datatype import DataType
from finn.custom_op.fpgadataflow.hlscustomop import HLSCustomOp
@@ -83,18 +83,11 @@ 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",
+ "RandomNormal",
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),
- ),
+ shape=list(oshape),
)
def infer_node_datatype(self, model):
diff --git a/src/finn/custom_op/fpgadataflow/fmpadding_batch.py b/src/finn/custom_op/fpgadataflow/fmpadding_batch.py
index ca0b2f12ab6e84bab0b87e5a34917619c2ba289d..ec2a2b690f0bb7d75d0e2a6a0581801557fec02f 100644
--- a/src/finn/custom_op/fpgadataflow/fmpadding_batch.py
+++ b/src/finn/custom_op/fpgadataflow/fmpadding_batch.py
@@ -1,7 +1,7 @@
import numpy as np
import os
import warnings
-from onnx import TensorProto, helper
+from onnx import helper
from finn.core.datatype import DataType
from finn.custom_op.fpgadataflow.hlscustomop import HLSCustomOp
@@ -99,18 +99,11 @@ 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",
+ "RandomNormal",
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),
- ),
+ shape=list(oshape),
)
def infer_node_datatype(self, model):
diff --git a/src/finn/custom_op/fpgadataflow/globalaccpool_batch.py b/src/finn/custom_op/fpgadataflow/globalaccpool_batch.py
index eabdcf599d23d35ed13069cb81afa3ec4999e8e7..d6f860db150c554804477243bc4b9582bdaba8b0 100644
--- a/src/finn/custom_op/fpgadataflow/globalaccpool_batch.py
+++ b/src/finn/custom_op/fpgadataflow/globalaccpool_batch.py
@@ -29,7 +29,7 @@
import numpy as np
import os
import warnings
-from onnx import TensorProto, helper
+from onnx import helper
from finn.core.datatype import DataType
from finn.custom_op.fpgadataflow.hlscustomop import HLSCustomOp
@@ -95,18 +95,11 @@ 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",
+ "RandomNormal",
inputs=[],
outputs=[self.onnx_node.output[0]],
- value=helper.make_tensor(
- name="const_tensor",
- data_type=TensorProto.FLOAT,
- dims=values.shape,
- vals=values.flatten(),
- ),
+ shape=list(oshape),
)
def infer_node_datatype(self, model):
diff --git a/src/finn/custom_op/fpgadataflow/iodma.py b/src/finn/custom_op/fpgadataflow/iodma.py
index 4fa74e35dbec902d6c4d980c48ffaa69cbd5ccd9..34842181c3d66a01a52a895fadb812ea939a5b0a 100644
--- a/src/finn/custom_op/fpgadataflow/iodma.py
+++ b/src/finn/custom_op/fpgadataflow/iodma.py
@@ -29,7 +29,7 @@
import math
import numpy as np
import warnings
-from onnx import TensorProto, helper
+from onnx import helper
from finn.core.datatype import DataType
from finn.custom_op.fpgadataflow.hlscustomop import HLSCustomOp
@@ -146,18 +146,11 @@ 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",
+ "RandomNormal",
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),
- ),
+ shape=list(oshape),
)
def infer_node_datatype(self, model):
diff --git a/src/finn/custom_op/fpgadataflow/labelselect_batch.py b/src/finn/custom_op/fpgadataflow/labelselect_batch.py
index d70d0f6a9b0cacb491ce748b84c8c7c474605170..4d34de8fcc3c0f6c9000b4c456e5a6d772d65245 100644
--- a/src/finn/custom_op/fpgadataflow/labelselect_batch.py
+++ b/src/finn/custom_op/fpgadataflow/labelselect_batch.py
@@ -102,18 +102,12 @@ 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(),
- ),
+ shape=list(oshape),
+ dtype=TensorProto.INT64,
)
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 cef964acd5192ad254e1086dacead590b51e7ec1..5bcc653597345eecde5db6270e02ded71ec7c4b1 100644
--- a/src/finn/custom_op/fpgadataflow/pool_batch.py
+++ b/src/finn/custom_op/fpgadataflow/pool_batch.py
@@ -28,7 +28,7 @@
import numpy as np
import os
-from onnx import TensorProto, helper
+from onnx import helper
from finn.core.datatype import DataType
from finn.custom_op.fpgadataflow.hlscustomop import HLSCustomOp
@@ -163,18 +163,11 @@ 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",
+ "RandomNormal",
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),
- ),
+ shape=list(oshape),
)
def infer_node_datatype(self, model):
diff --git a/src/finn/custom_op/fpgadataflow/streamingdatawidthconverter_batch.py b/src/finn/custom_op/fpgadataflow/streamingdatawidthconverter_batch.py
index 67e3cd36549d34cab55a931cc040fee5d14ca06f..aab87be52f5607b61fb0dbeb4b90ce90871fdb89 100644
--- a/src/finn/custom_op/fpgadataflow/streamingdatawidthconverter_batch.py
+++ b/src/finn/custom_op/fpgadataflow/streamingdatawidthconverter_batch.py
@@ -30,7 +30,7 @@ import math
import numpy as np
import os
import warnings
-from onnx import TensorProto, helper
+from onnx import helper
from finn.core.datatype import DataType
from finn.custom_op.fpgadataflow.hlscustomop import HLSCustomOp
@@ -165,18 +165,11 @@ 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",
+ "RandomNormal",
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),
- ),
+ shape=list(oshape),
)
def infer_node_datatype(self, model):
diff --git a/src/finn/custom_op/fpgadataflow/streamingfclayer_batch.py b/src/finn/custom_op/fpgadataflow/streamingfclayer_batch.py
index 96594d441345332bbe5873570156e07cacbb385d..2f1097d712df56f4557fb73f02951db8c6f80b2b 100644
--- a/src/finn/custom_op/fpgadataflow/streamingfclayer_batch.py
+++ b/src/finn/custom_op/fpgadataflow/streamingfclayer_batch.py
@@ -31,7 +31,7 @@ import numpy as np
import os
import textwrap
import warnings
-from onnx import TensorProto, helper
+from onnx import helper
from finn.core.datatype import DataType
from finn.custom_op.fpgadataflow.hlscustomop import HLSCustomOp
@@ -151,18 +151,11 @@ 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",
+ "RandomNormal",
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),
- ),
+ shape=list(oshape),
)
def infer_node_datatype(self, model):
diff --git a/src/finn/custom_op/fpgadataflow/streamingfifo.py b/src/finn/custom_op/fpgadataflow/streamingfifo.py
index 9653d698f54e2b97c66bff62c0b3c11057b36aad..ce09d5565a1b6cf1065a3274971fa663e8431c37 100644
--- a/src/finn/custom_op/fpgadataflow/streamingfifo.py
+++ b/src/finn/custom_op/fpgadataflow/streamingfifo.py
@@ -30,7 +30,7 @@ import numpy as np
import os
import subprocess
import warnings
-from onnx import TensorProto, helper
+from onnx import helper
from shutil import copy
from finn.core.datatype import DataType
@@ -78,18 +78,11 @@ 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",
+ "RandomNormal",
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),
- ),
+ shape=list(oshape),
)
def infer_node_datatype(self, model):
diff --git a/src/finn/custom_op/fpgadataflow/streamingmaxpool_batch.py b/src/finn/custom_op/fpgadataflow/streamingmaxpool_batch.py
index 19a42fe2d6b53879d401ec8bd462ddd59623dc1e..c0641ffb741a19926422ede85fd6ccabe0476fab 100644
--- a/src/finn/custom_op/fpgadataflow/streamingmaxpool_batch.py
+++ b/src/finn/custom_op/fpgadataflow/streamingmaxpool_batch.py
@@ -29,7 +29,7 @@
import numpy as np
import os
import warnings
-from onnx import TensorProto, helper
+from onnx import helper
from finn.core.datatype import DataType
from finn.custom_op.fpgadataflow.hlscustomop import HLSCustomOp
@@ -140,18 +140,11 @@ 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",
+ "RandomNormal",
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),
- ),
+ shape=list(oshape),
)
def infer_node_datatype(self, model):
diff --git a/src/finn/custom_op/fpgadataflow/thresholding_batch.py b/src/finn/custom_op/fpgadataflow/thresholding_batch.py
index 7fb7634dc22e1d00569e7bb755bf120d6de4f808..df562f5450d7e3f8a35c08b4da50abb4b14a807a 100644
--- a/src/finn/custom_op/fpgadataflow/thresholding_batch.py
+++ b/src/finn/custom_op/fpgadataflow/thresholding_batch.py
@@ -31,7 +31,7 @@ import os
import textwrap
import warnings
from math import ceil, log2
-from onnx import TensorProto, helper
+from onnx import helper
from finn.core.datatype import DataType
from finn.custom_op.fpgadataflow.hlscustomop import HLSCustomOp
@@ -112,18 +112,11 @@ 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",
+ "RandomNormal",
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),
- ),
+ shape=list(oshape),
)
def infer_node_datatype(self, model):
diff --git a/src/finn/custom_op/fpgadataflow/upsampler.py b/src/finn/custom_op/fpgadataflow/upsampler.py
index e8aa09b1c0754b68e37d01551afe90811f22e7cd..fcdf9d7d2c73c98cb2809334ac33b4488f0ea909 100644
--- a/src/finn/custom_op/fpgadataflow/upsampler.py
+++ b/src/finn/custom_op/fpgadataflow/upsampler.py
@@ -1,7 +1,7 @@
import numpy as np
import os
import warnings
-from onnx import TensorProto, helper
+from onnx import helper
from finn.core.datatype import DataType
from finn.custom_op.fpgadataflow.hlscustomop import HLSCustomOp
@@ -69,18 +69,11 @@ 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",
+ "RandomNormal",
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),
- ),
+ shape=list(oshape),
)
def infer_node_datatype(self, model):
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 9fc133b9bc09ca556df5779970d68d4b62131659..d9e7e566e6bc8883cf49a4064cc07e283251e1b3 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 +2,7 @@ import math
import numpy as np
import os
import warnings
-from onnx import TensorProto, helper
+from onnx import helper
from finn.core.datatype import DataType
from finn.custom_op.fpgadataflow.hlscustomop import HLSCustomOp
@@ -129,18 +129,11 @@ 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",
+ "RandomNormal",
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),
- ),
+ shape=list(oshape),
)
def infer_node_datatype(self, model):