Bump up CUDA version used for tests

dali/python/backend_impl.cc

@@ -155,11 +155,18 @@ void CheckContiguousTensor(const TStrides &strides, int num_strides,
   DALI_ENFORCE(num_strides == num_extents,
     "There should be exactly as many strides as there are extents in array shape.");
   int64_t stride_from_shape = element_size;
+  int64_t stride_from_shape_collapsed = 1;
+  int64_t last_non_one_dim = 1;
   for (int i = num_strides - 1; i >= 0; i--) {
-    DALI_ENFORCE(strides[i] == stride_from_shape,
+    DALI_ENFORCE(strides[i] == stride_from_shape || strides[i] == stride_from_shape_collapsed,
         make_string("Strided data not supported. Dimension ", i, " has stride ", strides[i],
         " whereas densely packed data of this shape would have a stride ", stride_from_shape));
     stride_from_shape *= shape[i];
+    // for shapes [1, 1, 5] leading dimensions may not contribute to
+    if (shape[i] != 1) {
+      stride_from_shape_collapsed *= last_non_one_dim;
+      last_non_one_dim = shape[i];
+    }
   }
 }
 

dali/test/python/test_torch_pipeline_rnnt.py

@@ -159,7 +159,9 @@ def forward(self, inp, seq_len):
         # do stft
         x = torch.stft(x, n_fft=self.n_fft, hop_length=self.hop_length,
                        win_length=self.win_length, pad_mode='reflect',
-                       center=True, window=self.window.to(dtype=torch.float).to(x.device))
+                       center=True, window=self.window.to(dtype=torch.float).to(x.device),
+                       return_complex=True)
+        x = torch.view_as_real(x)
 
         # get power spectrum
         x = x.pow(2).sum(-1)
@@ -214,7 +216,9 @@ def torch_spectrogram(audio, sample_rate, device='cpu',
     window_tensor = window_fn(win_length, periodic=False) if window_fn else None
     stft_out = torch.stft(audio, n_fft=n_fft, hop_length=hop_length,
                           win_length=win_length, pad_mode=pad_mode,
-                          center=center, window=window_tensor.to(dtype=torch.float))
+                          center=center, window=window_tensor.to(dtype=torch.float),
+                          return_complex=True)
+    stft_out = torch.view_as_real(stft_out)
     # get power spectrum
     spectrogram = stft_out.pow(2).sum(-1)
     spectrogram = spectrogram.cpu().numpy()

docs/examples/use_cases/paddle/index.py

@@ -2,6 +2,5 @@
     underline_char="=",
     entries=[
         "resnet50/paddle-resnet50.rst",
-        "ssd/paddle-ssd.rst",
         "tsm/paddle-tsm.rst",
     ])