Merge branch 'main' into updatepytests

Author: JanFSchulte

hls4ml/converters/pytorch_to_hls.py

@@ -95,6 +95,7 @@ def decorator(function):
     'avg_pool1d': 'AvgPool1d',
     'avg_pool2d': 'AvgPool2d',
     'flatten': 'Flatten',
+    'view': 'View',
 }
 
 

test/pytest/test_pytorch_api.py

@@ -810,3 +810,65 @@ def forward(self, x):
     hls_prediction = hls_model.predict(hls_input).flatten()
 
     np.testing.assert_allclose(hls_prediction, pytorch_prediction, rtol=0, atol=5e-2)
+
+
+@pytest.mark.parametrize('backend', ['Vivado', 'Vitis', 'Quartus'])
+@pytest.mark.parametrize('io_type', ['io_parallel', 'io_stream'])
+def test_view(backend, io_type):
+
+    class TestModel(nn.Module):
+        def __init__(self, n_in, n_out, size_in):
+            super().__init__()
+            self.view_mult = n_out * size_in
+
+            self.conv1 = nn.Conv1d(
+                n_in,
+                n_out,
+                kernel_size=3,
+                padding=1,
+                bias=False,
+            )
+
+        def forward(self, x):
+            z = self.conv1(x)
+            z = z.view(-1, self.view_mult)
+            return z
+
+    n_in = 2
+    n_out = 4
+    size_in = 128
+    n_batch = 100
+
+    model = TestModel(n_in, n_out, size_in)
+    model = model.to(memory_format=torch.channels_last)
+    model.eval()
+
+    X_input = np.random.rand(n_batch, n_in, size_in)
+    pytorch_prediction = model(torch.Tensor(X_input)).detach().numpy()
+
+    # X_input is channels last
+    X_input = np.ascontiguousarray(X_input.transpose(0, 2, 1))
+    config = config_from_pytorch_model(model, inputs_channel_last=True, transpose_outputs=False)
+
+    output_dir = str(test_root_path / f'hls4mlprj_pytorch_view_{backend}_{io_type}')
+    hls_model = convert_from_pytorch_model(
+        model,
+        (None, n_in, size_in),
+        hls_config=config,
+        output_dir=output_dir,
+        backend=backend,
+        io_type=io_type,
+    )
+
+    hls_model.compile()
+
+    # reshape hls prediction to channels last, then transpose, then reshape
+    # to match .view
+    hls_prediction = np.reshape(
+        np.transpose(np.reshape(hls_model.predict(X_input), (n_batch, size_in, n_out)), (0, 2, 1)),
+        (n_batch, size_in * n_out),
+    )
+
+    rtol = 0
+    atol = 5.0e-2
+    np.testing.assert_allclose(hls_prediction, pytorch_prediction, rtol=rtol, atol=atol)