Use ascii char for tolerance; remove redundant forward calls in memor…

…y_encoder; fix test_with_tolerance function

tripy/examples/segment-anything-model-v2/README.md

@@ -25,9 +25,9 @@ This is an implementation of SAM2 model ([original repository](https://github.co
     <!-- Tripy: TEST: EXPECTED_STDOUT Start -->
     <!--
     ```
-    Generating image embedding took {137.81±10%} ms per run (averaged over 100 runs, with 5 warmup runs)
-    Predicting masks took {37.78±10%} ms per run (averaged over 100 runs, with 5 warmup runs)
-    Scores for each prediction: {0.78759766±5%} {0.640625±5%} {0.05099487±5%}
+    Generating image embedding took {137.81~10%} ms per run (averaged over 100 runs, with 5 warmup runs)
+    Predicting masks took {37.78~10%} ms per run (averaged over 100 runs, with 5 warmup runs)
+    Scores for each prediction: {0.78759766~5%} {0.640625~5%} {0.05099487~5%}
     ```
      -->
     <!-- Tripy: TEST: EXPECTED_STDOUT End -->

tripy/examples/segment-anything-model-v2/sam2/modeling/memory_encoder.py

@@ -74,12 +74,8 @@ def __init__(
         self.encoder.append(tp.Conv(mask_out_chans, embed_dim, kernel_dims=(1, 1), dtype=self.dtype))
 
     def __call__(self, x):
-        return self.forward(x)
-
-    def forward(self, x):
         for l in self.encoder:
             x = l(x)
-
         return x
 
 
@@ -117,9 +113,6 @@ def __init__(
         self.drop_path = Dummy()
 
     def __call__(self, x):
-        return self.forward(x)
-
-    def forward(self, x):
         input = x
         x = self.dwconv(x)
         x = self.norm(x)
@@ -146,9 +139,6 @@ def __init__(self, layer, num_layers, dim=None, input_projection=False, dtype="f
             self.proj = tp.Conv(dim, dim, kernel_dims=(1, 1), dtype=self.dtype)
 
     def __call__(self, x):
-        return self.forward(x)
-
-    def forward(self, x):
         x = self.proj(x)
         for layer in self.layers:
             x = layer(x)
@@ -175,14 +165,6 @@ def __call__(
         pix_feat: tp.Tensor,
         masks: tp.Tensor,
         skip_mask_sigmoid: bool = False,
-    ):
-        return self.forward(pix_feat, masks, skip_mask_sigmoid)
-
-    def forward(
-        self,
-        pix_feat: tp.Tensor,
-        masks: tp.Tensor,
-        skip_mask_sigmoid: bool = False,
     ) -> Tuple[tp.Tensor, tp.Tensor]:
         if not skip_mask_sigmoid:
             masks = tp.sigmoid(masks)

tripy/examples/segment-anything-model-v2/sam2/modeling/sam2_utils.py

@@ -14,7 +14,7 @@
 # limitations under the License.
 
 import copy
-from typing import Optional
+from typing import Optional, Callable
 
 import torch
 import torch.nn as nn
@@ -150,7 +150,7 @@ def __init__(
         hidden_dim: int,
         output_dim: int,
         num_layers: int,
-        activation: tp.Module = tp.relu,
+        activation: Callable[[tp.Tensor], tp.Tensor] = tp.relu,
         sigmoid_output: bool = False,
         dtype=tp.float32,
     ) -> None:

tripy/tests/test_examples.py

@@ -87,18 +87,12 @@ def __str__(self):
 @pytest.mark.parametrize("example", EXAMPLES, ids=lambda case: str(case))
 def test_examples(example, sandboxed_install_run):
 
-    def test_with_tolerance(number, value, tolerance):
-        tolerance = float(tolerance) / 100
-        lower = float(number) * (1 - tolerance)
-        upper = float(number) * (1 + tolerance)
-        try:
-            return lower <= float(value) <= upper
-        except ValueError:
-            return False
+    def test_with_tolerance(expected, actual, tolerance):
+        return (abs(float(actual) - float(expected)) / float(expected)) * 100 <= tolerance
 
     def process_tolerances(expected_output):
         specs = []
-        placeholder_regex = r"{(\d+\.?\d*)±(\d+)%}"
+        placeholder_regex = r"{(\d+\.?\d*)~(\d+)%}"
         pattern = expected_output
 
         # Replace tolerance patterns with more flexible capture group