diff --git a/tests/test_layer_streaming_fclayer_batch.py b/tests/test_layer_streaming_fclayer_batch.py
deleted file mode 100644
index 06fb9a8f1d761a519030b9336c093445ea2a7ea6..0000000000000000000000000000000000000000
--- a/tests/test_layer_streaming_fclayer_batch.py
+++ /dev/null
@@ -1,66 +0,0 @@
-# import onnx
-import numpy as np
-from onnx import TensorProto, helper
-
-import finn.core.onnx_exec as oxe
-from finn.core.datatype import DataType
-from finn.core.modelwrapper import ModelWrapper
-
-
-def test_fclayer_batch():
- inp = helper.make_tensor_value_info("inp", TensorProto.FLOAT, [1, 2, 8])
- outp = helper.make_tensor_value_info("outp", TensorProto.FLOAT, [1, 4, 4])
-
- FCLayer_node = helper.make_node(
- "StreamingFCLayer_Batch",
- ["inp", "weights", "thresh"],
- ["outp"],
- domain="finn",
- backend="fpgadataflow",
- resType="ap_resource_lut()",
- MW=16,
- MH=16,
- SIMD=8,
- PE=4,
- resDataType="Recast<XnorMul>",
- )
-
- graph = helper.make_graph(
- nodes=[FCLayer_node],
- name="fclayer_graph",
- inputs=[inp],
- outputs=[outp],
- value_info=[
- helper.make_tensor_value_info("weights", TensorProto.FLOAT, [8, 4, 16]),
- helper.make_tensor_value_info("thresh", TensorProto.FLOAT, [16, 4, 3]),
- ],
- )
-
- model = helper.make_model(graph, producer_name="fclayer-model")
- model = ModelWrapper(model)
-
- # set the tensor datatypes (in this case: all to bipolar)
- for tensor in graph.input:
- model.set_tensor_datatype(tensor.name, DataType["BIPOLAR"])
- for tensor in graph.output:
- model.set_tensor_datatype(tensor.name, DataType["BIPOLAR"])
-
- # onnx.save(model.model, "fclayer-model.onnx")
-
- # generate input data
- input_tensor = np.random.randint(2, size=16)
- input_tensor = (np.asarray(input_tensor, dtype=np.float32)).reshape(1, 2, 8)
- input_dict = {"inp": input_tensor}
-
- # generate weights
- weights_tensor = np.random.randint(2, size=512)
- weights_tensor = (np.asarray(weights_tensor, dtype=np.float32)).reshape(8, 4, 16)
- input_dict["weights"] = weights_tensor
-
- # generate threshold activation
- thresh_tensor = np.random.randint(2, size=192)
- thresh_tensor = (np.asarray(thresh_tensor, dtype=np.float32)).reshape(16, 4, 3)
- input_dict["thresh"] = thresh_tensor
-
- output_dict = oxe.execute_onnx(model, input_dict)
- print(output_dict)
diff --git a/tests/test_layer_streaming_maxpool.py b/tests/test_layer_streaming_maxpool.py
deleted file mode 100644
index 4c9806a3571a5455fed23febf816b7e94a47c188..0000000000000000000000000000000000000000
--- a/tests/test_layer_streaming_maxpool.py
+++ /dev/null
@@ -1,80 +0,0 @@
-# import onnx
-import numpy as np
-from onnx import TensorProto, helper
-
-import finn.core.onnx_exec as oxe
-from finn.core.datatype import DataType
-from finn.core.modelwrapper import ModelWrapper
-
-
-def test_layer_streaming_maxpool():
- inp = helper.make_tensor_value_info("in", TensorProto.FLOAT, [2, 4, 4])
- outp = helper.make_tensor_value_info("out", TensorProto.FLOAT, [2, 2, 2])
-
- MaxPool_node = helper.make_node(
- "StreamingMaxPool",
- ["in"],
- ["out"],
- domain="finn",
- backend="fpgadataflow",
- ImgDim=4,
- PoolDim=2,
- NumChannels=2,
- )
-
- graph = helper.make_graph(
- nodes=[MaxPool_node], name="max_pool_graph", inputs=[inp], outputs=[outp],
- )
- model = helper.make_model(graph, producer_name="finn-hls-onnx-model")
- model = ModelWrapper(model)
-
- # set the tensor datatypes (in this case: all to bipolar)
- for tensor in graph.input:
- model.set_tensor_datatype(tensor.name, DataType["BIPOLAR"])
- for tensor in graph.output:
- model.set_tensor_datatype(tensor.name, DataType["BIPOLAR"])
-
- # onnx.save(model.model, "max-pool-model.onnx")
-
- input_tensor = np.asarray(
- [
- 1,
- 1,
- 1,
- 1,
- 1,
- 1,
- 1,
- 1,
- 0,
- 0,
- 0,
- 0,
- 0,
- 0,
- 0,
- 0,
- 0,
- 0,
- 0,
- 0,
- 0,
- 0,
- 0,
- 0,
- 1,
- 1,
- 1,
- 1,
- 1,
- 1,
- 1,
- 1,
- ],
- dtype=np.float32,
- ).reshape(2, 4, 4)
- print(input_tensor)
-
- input_dict = {"in": input_tensor}
- output_dict = oxe.execute_onnx(model, input_dict)
- print(output_dict)
diff --git a/tests/test_layer_streaming_maxpool_batch.py b/tests/test_layer_streaming_maxpool_batch.py
deleted file mode 100644
index 75cbd64376055996de58455c1d28530b770500a5..0000000000000000000000000000000000000000
--- a/tests/test_layer_streaming_maxpool_batch.py
+++ /dev/null
@@ -1,114 +0,0 @@
-import numpy as np
-from onnx import TensorProto, helper
-
-import finn.core.onnx_exec as oxe
-from finn.core.datatype import DataType
-from finn.core.modelwrapper import ModelWrapper
-
-
-def test_layer_streaming_maxpool_batch():
- inp = helper.make_tensor_value_info("in", TensorProto.FLOAT, [2, 2, 4, 4])
- outp = helper.make_tensor_value_info("out", TensorProto.FLOAT, [2, 2, 2, 2])
-
- MaxPool_batch_node = helper.make_node(
- "StreamingMaxPool_Batch",
- ["in"],
- ["out"],
- domain="finn",
- backend="fpgadataflow",
- ImgDim=4,
- PoolDim=2,
- NumChannels=2,
- )
-
- graph = helper.make_graph(
- nodes=[MaxPool_batch_node],
- name="max_pool_batch_graph",
- inputs=[inp],
- outputs=[outp],
- )
- model = helper.make_model(graph, producer_name="finn-hls-onnx-model")
- model = ModelWrapper(model)
-
- # set the tensor datatypes (in this case: all to bipolar)
- for tensor in graph.input:
- model.set_tensor_datatype(tensor.name, DataType["BIPOLAR"])
- for tensor in graph.output:
- model.set_tensor_datatype(tensor.name, DataType["BIPOLAR"])
-
- # onnx.save(model.model, "max-pool-model.onnx")
-
- input_tensor = np.asarray(
- [
- 1,
- 1,
- 1,
- 1,
- 1,
- 1,
- 1,
- 1,
- 0,
- 0,
- 0,
- 0,
- 0,
- 0,
- 0,
- 0,
- 0,
- 0,
- 0,
- 0,
- 0,
- 0,
- 0,
- 0,
- 1,
- 1,
- 1,
- 1,
- 1,
- 1,
- 1,
- 1,
- 1,
- 1,
- 1,
- 1,
- 1,
- 1,
- 1,
- 1,
- 0,
- 0,
- 0,
- 0,
- 0,
- 0,
- 0,
- 0,
- 0,
- 0,
- 0,
- 0,
- 0,
- 0,
- 0,
- 0,
- 1,
- 1,
- 1,
- 1,
- 1,
- 1,
- 1,
- 1,
- ],
- dtype=np.float32,
- ).reshape(2, 2, 4, 4)
- print(input_tensor)
-
- input_dict = {"in": input_tensor}
- output_dict = oxe.execute_onnx(model, input_dict)
- print(output_dict)