fix most pytest issues

Author: JanFSchulte

hls4ml/templates/quartus/firmware/nnet_utils/nnet_conv1d_resource.h

@@ -3,6 +3,7 @@
 
 #include "nnet_common.h"
 #include "nnet_dense.h"
+#include <cstdint>
 
 namespace nnet {
 

hls4ml/templates/quartus/firmware/nnet_utils/nnet_conv2d_resource.h

@@ -4,6 +4,7 @@
 #include "nnet_common.h"
 #include "nnet_dense.h"
 #include "nnet_helpers.h"
+#include <cstdint>
 
 namespace nnet {
 

test/pytest/test_pytorch_api.py

@@ -550,7 +550,7 @@ def test_pooling(pooling, padds, backend):
     # Verify correct parsing of layer
     hls_pool = list(hls_model.get_layers())[-2]
     if '2d' in pooling.__name__:
-        assert hls_pool.attributes['name'] == poolNode.name
+        assert hls_pool.attributes['name'] == "_" + poolNode.name.split("_")[-1]
         assert hls_pool.attributes['class_name'][-2] == str(2)
         assert hls_pool.attributes['stride_height'] == class_object_pool.stride
         assert hls_pool.attributes['stride_width'] == class_object_pool.stride
@@ -560,14 +560,14 @@ def test_pooling(pooling, padds, backend):
 
     elif '1d' in pooling.__name__:
         if "Max" in pooling.__name__:
-            assert hls_pool.attributes['name'] == poolNode.name
+            assert hls_pool.attributes['name'] == "_" + poolNode.name.split("_")[-1]
             assert hls_pool.attributes['class_name'][-2] == str(1)
             assert hls_pool.attributes['pool_width'] == class_object_pool.kernel_size
             assert hls_pool.attributes['stride_width'] == class_object_pool.stride
             assert hls_pool.attributes['padding'] == 'valid' if class_object_pool.padding == 0 else 'same'
 
         else:
-            assert hls_pool.attributes['name'] == poolNode.name
+            assert hls_pool.attributes['name'] == "_" + poolNode.name.split("_")[-1]
             assert hls_pool.attributes['class_name'][-2] == str(1)
             assert hls_pool.attributes['pool_width'] == class_object_pool.kernel_size[0]
             assert hls_pool.attributes['stride_width'] == class_object_pool.stride[0]
@@ -641,7 +641,7 @@ def test_squeeze(backend, io_type):
     pytorch_prediction = model(torch.Tensor(X_input)).detach().numpy().flatten()
 
     config = config_from_pytorch_model(model)
-    del config['Model']['InputsChannelLast']  # We don't want anything touched for this test
+    del config['Model']['ChannelsLastConversion']  # We don't want anything touched for this test
     output_dir = str(test_root_path / f'hls4mlprj_pytorch_api_squeeze_{backend}_{io_type}')
 
     hls_model = convert_from_pytorch_model(
@@ -719,7 +719,7 @@ def test_skipped_layers(backend, io_type):
     input_shape = (3, 8)
     batch_input_shape = (None,) + input_shape
     config = config_from_pytorch_model(
-        model, default_precision='ap_fixed<32,16>', inputs_channel_last=True, transpose_outputs=False
+        model, default_precision='ap_fixed<32,16>', channels_last_conversion="full", transpose_outputs=False
     )
     output_dir = str(test_root_path / f'hls4mlprj_pytorch_api_skipped_{backend}_{io_type}')
     hls_model = convert_from_pytorch_model(
@@ -734,10 +734,9 @@ def test_skipped_layers(backend, io_type):
     hls_model.compile()
 
     input = torch.randn(10, 3, 8)
-    hls_input = np.ascontiguousarray(torch.permute(input, (0, 2, 1)).detach().numpy())  # Transpose to channels_last
 
     pytorch_prediction = model(input).detach().numpy().flatten()
-    hls_prediction = hls_model.predict(hls_input).flatten()
+    hls_prediction = hls_model.predict(input.detach().numpy()).flatten()
 
     np.testing.assert_allclose(hls_prediction, pytorch_prediction, rtol=0, atol=5e-2)
 
@@ -785,8 +784,7 @@ def forward(self, x):
     config = config_from_pytorch_model(
         model,
         default_precision='ap_fixed<32,16>',
-        inputs_channel_last=False,  # Crucial for testing if the first Transpose was removed
-        transpose_outputs=False,
+        channels_last_conversion="full",  # Crucial for testing if the first Transpose was removed
     )
     output_dir = str(test_root_path / f'hls4mlprj_pytorch_api_transpose_nop_{tensor_rank}d_{backend}_{io_type}')
     hls_model = convert_from_pytorch_model(