diff --git a/src/finn/transformation/move_reshape.py b/src/finn/transformation/move_reshape.py
index d723a530b75f2fcad4ee469a957bd6c4ce348411..990b858ad62aec00be4be4e0dd30bef3eb9e3ce3 100644
--- a/src/finn/transformation/move_reshape.py
+++ b/src/finn/transformation/move_reshape.py
@@ -1,6 +1,7 @@
from finn.transformation.base import Transformation
from finn.util.basic import get_by_name, is_finn_op
from finn.custom_op.registry import getCustomOp
+import warnings
def _is_fpgadataflow_node(node):
@@ -19,8 +20,9 @@ def _is_fpgadataflow_node(node):
class RemoveCNVtoFCFlatten(Transformation):
- """Removes a node that implements a (1, -1) reshape if it is
- between two fpgadataflow nodes"""
+ """Removes a flatten node if it is between two fpgadataflow nodes.
+ For an NHWC-Conv to FC transition, the preceding transpose is absorbed.
+ The flatten operation can also be implemented by a reshape node."""
def apply(self, model):
graph = model.graph
@@ -61,16 +63,10 @@ class RemoveCNVtoFCFlatten(Transformation):
assert (
W is not None
), "Initializer for matmul weights is not set."
- # print("fc weights before")
- # print(W.shape)
- # print(W)
W_new = W.reshape(c, h, w, mh)
W_new = W_new.transpose((1, 2, 0, 3))
W_new = W_new.reshape(mw, mh)
model.set_initializer(consumer.input[1], W_new)
- # print("fc weights after")
- # print(W_new.shape)
- # print(W_new)
# remove transpose & flatten nodes
consumer.input[0] = transp_node.input[0]
graph.node.remove(n)
@@ -78,7 +74,8 @@ class RemoveCNVtoFCFlatten(Transformation):
graph_modified = True
else:
warnings.warn(
- "Could not absorb transpose->flatten into subsequent node"
+ "Could not absorb transpose->flatten \
+ into subsequent node"
)
else:
warnings.warn(
diff --git a/src/finn/transformation/streamline/absorb.py b/src/finn/transformation/streamline/absorb.py
index e8082eb820cc0be94fd448062493f6425c83ef41..9b842162f7f751c60b18bbd288ff96ef28d3aa88 100644
--- a/src/finn/transformation/streamline/absorb.py
+++ b/src/finn/transformation/streamline/absorb.py
@@ -367,7 +367,6 @@ class AbsorbTransposeIntoMultiThreshold(Transformation):
)
graph.node.insert(node_ind + 1, new_transpose)
final_t_cand.input[0] = transpose_output
-
graph_modified = True
if graph_modified:
model = model.transform(InferDataTypes())
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 5d4d5a2a34f3c1ab4223c8d6bec2b530f99e470d..a67a22fed294a1715293d42b824dc895db752496 100755
--- a/tests/fpgadataflow/test_convert_to_hls_conv_fc_transition.py
+++ b/tests/fpgadataflow/test_convert_to_hls_conv_fc_transition.py
@@ -36,9 +36,6 @@ from finn.transformation.infer_datatypes import InferDataTypes
from finn.transformation.general import GiveUniqueNodeNames
from finn.transformation.lower_convs_to_matmul import LowerConvsToMatMul
-from finn.transformation.fpgadataflow.prepare_ip import PrepareIP
-from finn.transformation.fpgadataflow.prepare_rtlsim import PrepareRTLSim
-from finn.transformation.fpgadataflow.hlssynth_ip import HLSSynthIP
import finn.core.onnx_exec as oxe
from finn.core.modelwrapper import ModelWrapper
from finn.util.basic import gen_finn_dt_tensor
@@ -48,24 +45,13 @@ from finn.transformation.fpgadataflow.prepare_cppsim import PrepareCppSim
from finn.transformation.fpgadataflow.compile_cppsim import CompileCppSim
from finn.transformation.fpgadataflow.set_exec_mode import SetExecMode
from finn.custom_op.general.im2col import compute_conv_output_dim
-from finn.custom_op.registry import getCustomOp
-from finn.analysis.fpgadataflow.exp_cycles_per_layer import exp_cycles_per_layer
import finn.transformation.streamline.absorb as absorb
-from finn.transformation.fold_constants import FoldConstants
-from finn.transformation.general import (
- ApplyConfig,
- GiveReadableTensorNames,
- RemoveUnusedTensors,
- RemoveStaticGraphInputs,
-)
+from finn.transformation.general import RemoveUnusedTensors
from finn.transformation.streamline import Streamline
from finn.transformation.infer_data_layouts import InferDataLayouts
from finn.transformation.move_reshape import RemoveCNVtoFCFlatten
-from finn.transformation.streamline.reorder import (
- MakeMaxPoolNHWC,
- MoveScalarLinearPastInvariants,
-)
+from finn.transformation.streamline.reorder import MoveScalarLinearPastInvariants
import finn.core.data_layout as DataLayout
@@ -73,7 +59,7 @@ import finn.core.data_layout as DataLayout
def get_multithreshold_rand_params(channels, num_of_thres, seed=None):
if seed is not None:
np.random.seed(seed)
- steps = np.random.rand(channels, 1) * 20
+ steps = np.random.rand(channels, 1) * 30
bias = np.random.rand(channels, 1) * -10
thres = [np.arange(num_of_thres) for chn in range(channels)]
thres = ((thres + bias) * steps).astype(np.float32)
@@ -86,21 +72,19 @@ def get_multithreshold_rand_params(channels, num_of_thres, seed=None):
"conv_config",
[
((6, 6), (3, 3), (1, 1), (1, 1)),
- ((12, 1), (3, 1), (1, 1), (1, 0)),
- ((1, 15), (1, 5), (1, 1), (0, 2)),
+ # TODO: enable 1d conv test cases
+ # ((12, 1), (3, 1), (1, 1), (1, 0)),
+ # ((1, 15), (1, 5), (1, 1), (0, 2)),
],
)
@pytest.mark.parametrize("depthwise", [False, True])
@pytest.mark.parametrize("use_reshape", [False, True])
-@pytest.mark.parametrize("exec_mode", ["cppsim", "rtlsim"])
-@pytest.mark.slow
-@pytest.mark.vivado
-def test_convert_to_hls_conv_fc_transition(
- conv_config, depthwise, use_reshape, exec_mode
-):
+def test_convert_to_hls_conv_fc_transition(conv_config, depthwise, use_reshape):
np.random.seed(0)
idt = DataType.UINT4
odt = DataType.UINT4
+ conv_weight_dt = DataType.INT4
+ fc_weight_dt = DataType.INT4
input_shape, kernel_shape, stride, pad = conv_config
kernel_size_h, kernel_size_w = kernel_shape
@@ -127,13 +111,8 @@ def test_convert_to_hls_conv_fc_transition(
)
input_shape = [1, in_chn, input_size_h, input_size_w]
- conv_output_shape = [1, out_chn, output_size_h, output_size_w]
- output_shape = [1, fc_filters]
-
fc_param_shape = [out_chn * output_size_h * output_size_w, fc_filters]
-
- conv_weight_dt = DataType.INT4
- fc_weight_dt = DataType.INT4
+ output_shape = [1, fc_filters]
conv_config = {}
conv_config["dilations"] = [1, 1]
@@ -230,20 +209,15 @@ def test_convert_to_hls_conv_fc_transition(
model = model.transform(InferDataTypes())
model = model.transform(InferDataLayouts())
- model.save("testmodel_in.onnx")
-
# streamlining
new_model = model.transform(MoveScalarLinearPastInvariants())
new_model = new_model.transform(Streamline())
new_model = new_model.transform(LowerConvsToMatMul())
new_model = new_model.transform(absorb.AbsorbTransposeIntoMultiThreshold())
new_model = new_model.transform(Streamline())
-
new_model = new_model.transform(InferDataLayouts())
new_model = new_model.transform(RemoveUnusedTensors())
- new_model.save("testmodel_streamlined.onnx")
-
# convert_to_hls
if depthwise is True:
new_model = new_model.transform(to_hls.InferVVAU())
@@ -253,24 +227,13 @@ def test_convert_to_hls_conv_fc_transition(
new_model = new_model.transform(to_hls.InferStreamingMaxPool())
new_model = new_model.transform(RemoveCNVtoFCFlatten())
new_model = new_model.transform(absorb.AbsorbConsecutiveTransposes())
-
new_model = new_model.transform(GiveUniqueNodeNames())
new_model = new_model.transform(InferDataLayouts())
- if exec_mode == "cppsim":
- new_model = new_model.transform(PrepareCppSim())
- new_model = new_model.transform(CompileCppSim())
- new_model = new_model.transform(SetExecMode("cppsim"))
- elif exec_mode == "rtlsim":
- new_model = new_model.transform(SetExecMode("rtlsim"))
- new_model = new_model.transform(GiveUniqueNodeNames())
- new_model = new_model.transform(PrepareIP("xc7z020clg400-1", 5))
- new_model = new_model.transform(HLSSynthIP())
- new_model = new_model.transform(PrepareRTLSim())
- else:
- raise Exception("Unknown exec_mode")
-
- new_model.save("testmodel_hls.onnx")
+ # prepare cppsim
+ new_model = new_model.transform(PrepareCppSim())
+ new_model = new_model.transform(CompileCppSim())
+ new_model = new_model.transform(SetExecMode("cppsim"))
# check for correct execution
x = gen_finn_dt_tensor(idt, input_shape)