From 1aa05dc1dfd48b9bcea847bc0d75811706a508c7 Mon Sep 17 00:00:00 2001
From: Yaman Umuroglu <yamanu@xilinx.com>
Date: Fri, 20 May 2022 13:47:01 +0200
Subject: [PATCH] [Refactor] explicitly specify np datatype for new arrays
---
.../custom_op/fpgadataflow/channelwise_op_batch.py | 2 +-
.../fpgadataflow/convert_to_hls_layers.py | 6 ++++--
.../test_convert_to_hls_conv_fc_transition.py | 2 +-
.../test_convert_to_hls_layers_synthetic.py | 12 ++++++------
.../streamline/test_linear_past_eltwise.py | 9 +++++----
.../test_move_maxpool_past_multithreshold.py | 2 +-
6 files changed, 18 insertions(+), 15 deletions(-)
diff --git a/src/finn/custom_op/fpgadataflow/channelwise_op_batch.py b/src/finn/custom_op/fpgadataflow/channelwise_op_batch.py
index f6c562454..630a231c8 100644
--- a/src/finn/custom_op/fpgadataflow/channelwise_op_batch.py
+++ b/src/finn/custom_op/fpgadataflow/channelwise_op_batch.py
@@ -51,7 +51,7 @@ from . import templates
def get_smallest_possible(vals):
"""Returns smallest (fewest bits) possible DataType that can represent
value. Prefers unsigned integers where possible."""
- vals = np.array(vals)
+ vals = np.array(vals, dtype=np.float64)
for v in vals:
assert int(v) == v, "Error float value"
diff --git a/src/finn/transformation/fpgadataflow/convert_to_hls_layers.py b/src/finn/transformation/fpgadataflow/convert_to_hls_layers.py
index 46a97f6fa..a1a0c393f 100644
--- a/src/finn/transformation/fpgadataflow/convert_to_hls_layers.py
+++ b/src/finn/transformation/fpgadataflow/convert_to_hls_layers.py
@@ -1269,7 +1269,7 @@ class InferChannelwiseLinearLayer(Transformation):
def get_smallest_possible(self, vals):
"""Returns smallest (fewest bits) possible DataType that can represent
value. Prefers unsigned integers where possible."""
- vals = np.array(vals)
+ vals = np.array(vals, dtype=np.float64)
for v in vals:
assert int(v) == v, "Error float value"
@@ -1545,7 +1545,9 @@ class InferGlobalAccPoolLayer(Transformation):
model.make_new_valueinfo_name(), TensorProto.FLOAT, [1]
)
model.graph.value_info.append(mul_value)
- model.set_initializer(mul_value.name, np.array(1 / (vecs[1] * vecs[2])))
+ model.set_initializer(
+ mul_value.name, np.array(1 / (vecs[1] * vecs[2]), dtype=np.float32)
+ )
new_mul = helper.make_node(
"Mul",
[pool_out, mul_value.name],
diff --git a/tests/fpgadataflow/test_convert_to_hls_conv_fc_transition.py b/tests/fpgadataflow/test_convert_to_hls_conv_fc_transition.py
index 9b0f3d68a..64d0b3a84 100755
--- a/tests/fpgadataflow/test_convert_to_hls_conv_fc_transition.py
+++ b/tests/fpgadataflow/test_convert_to_hls_conv_fc_transition.py
@@ -201,7 +201,7 @@ def test_convert_to_hls_conv_fc_transition(conv_config, depthwise, use_reshape):
model.set_initializer(
"matmul_param", gen_finn_dt_tensor(fc_weight_dt, fc_param_shape)
)
- model.set_initializer("reshape_shape", np.array([1, -1]))
+ model.set_initializer("reshape_shape", np.array([1, -1], dtype=np.int64))
model = model.transform(InferShapes())
model = model.transform(InferDataTypes())
diff --git a/tests/fpgadataflow/test_convert_to_hls_layers_synthetic.py b/tests/fpgadataflow/test_convert_to_hls_layers_synthetic.py
index 608990156..f1d3bf5aa 100644
--- a/tests/fpgadataflow/test_convert_to_hls_layers_synthetic.py
+++ b/tests/fpgadataflow/test_convert_to_hls_layers_synthetic.py
@@ -127,12 +127,12 @@ def make_model(ch, ifmdim):
model = ModelWrapper(model)
# set initializers for scalar add/mul nodes
- model.set_initializer(add0_node.input[1], np.array([0.0]))
- model.set_initializer(add1_node.input[1], np.array([7.0]))
- model.set_initializer(add2_node.input[1], np.array([8.0]))
- model.set_initializer(mul1_node.input[1], np.array([2.0]))
- model.set_initializer(mul2_node.input[1], np.array([2.0]))
- model.set_initializer(reshape_node.input[1], np.array([1, -1]))
+ model.set_initializer(add0_node.input[1], np.array([0.0], dtype=np.float32))
+ model.set_initializer(add1_node.input[1], np.array([7.0], dtype=np.float32))
+ model.set_initializer(add2_node.input[1], np.array([8.0], dtype=np.float32))
+ model.set_initializer(mul1_node.input[1], np.array([2.0], dtype=np.float32))
+ model.set_initializer(mul2_node.input[1], np.array([2.0], dtype=np.float32))
+ model.set_initializer(reshape_node.input[1], np.array([1, -1], dtype=np.int64))
return model
diff --git a/tests/transformation/streamline/test_linear_past_eltwise.py b/tests/transformation/streamline/test_linear_past_eltwise.py
index 098b3f9d4..2cdb9dfc5 100644
--- a/tests/transformation/streamline/test_linear_past_eltwise.py
+++ b/tests/transformation/streamline/test_linear_past_eltwise.py
@@ -40,6 +40,7 @@ from finn.transformation.infer_shapes import InferShapes
from finn.transformation.streamline.reorder import MoveLinearPastEltwiseAdd
export_onnx_path = "test_linear_past_eltwise.onnx"
+np_default_dtype = np.float32
# construct a synthetic graph to test:
# topk insertion, topk conversion to hls, add conversion to hls
@@ -81,10 +82,10 @@ def make_model(shape):
model = ModelWrapper(model)
# set initializers for scalar add/mul nodes
- model.set_initializer(add1_node.input[1], np.array([7.0]))
- model.set_initializer(add2_node.input[1], np.array([8.0]))
- model.set_initializer(mul1_node.input[1], np.array([3.0]))
- model.set_initializer(mul2_node.input[1], np.array([3.0]))
+ model.set_initializer(add1_node.input[1], np.array([7.0], dtype=np_default_dtype))
+ model.set_initializer(add2_node.input[1], np.array([8.0], dtype=np_default_dtype))
+ model.set_initializer(mul1_node.input[1], np.array([3.0], dtype=np_default_dtype))
+ model.set_initializer(mul2_node.input[1], np.array([3.0], dtype=np_default_dtype))
return model
diff --git a/tests/transformation/streamline/test_move_maxpool_past_multithreshold.py b/tests/transformation/streamline/test_move_maxpool_past_multithreshold.py
index fca05afa5..d07f5d275 100644
--- a/tests/transformation/streamline/test_move_maxpool_past_multithreshold.py
+++ b/tests/transformation/streamline/test_move_maxpool_past_multithreshold.py
@@ -82,7 +82,7 @@ def test_move_maxpool_past_multithreshold():
model = model.transform(InferShapes())
model = model.transform(InferDataTypes())
- model.set_initializer("thres1", np.array([[0]]))
+ model.set_initializer("thres1", np.array([[0]], dtype=np.float32))
model.set_initializer(
"thres2", get_multithreshold_rand_params(*thres2_shape, seed=0)
)
--
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