apply Black 25.11.0 style in fbcode/deeplearning/projects (21/92)

Summary:
Formats the covered files with pyfmt.

paintitblack

Reviewed By: itamaro

Differential Revision: D90476315

fbshipit-source-id: ee94c471788b8e7d067813d8b3e0311214d17f3f

sam3/agent/agent_core.py

@@ -296,9 +296,9 @@ def agent_inference(
             assert LATEST_SAM3_TEXT_PROMPT != ""
 
             # Make sure that the last message is a image
-            assert (
-                messages[-1]["content"][1]["type"] == "image"
-            ), "Second content element should be an image"
+            assert messages[-1]["content"][1]["type"] == "image", (
+                "Second content element should be an image"
+            )
             messages.pop()  # Remove the last user message
             # Add simplified replacement message
             simplified_message = {
@@ -318,7 +318,7 @@ def agent_inference(
 
             # MLLM check the mask one by one
             for i in range(num_masks):
-                print(f"🔍 Checking mask {i+1}/{num_masks}...")
+                print(f"🔍 Checking mask {i + 1}/{num_masks}...")
                 image_w_mask_i, image_w_zoomed_in_mask_i = visualize(current_outputs, i)
 
                 image_w_zoomed_in_mask_i_path = os.path.join(
@@ -363,7 +363,7 @@ def agent_inference(
                     raise ValueError(
                         "Generated text is None, which is unexpected. Please check the Qwen server and the input parameters."
                     )
-                print(f"Generated text for mask {i+1}: {checking_generated_text}")
+                print(f"Generated text for mask {i + 1}: {checking_generated_text}")
                 verdict = (
                     checking_generated_text.split("<verdict>")[-1]
                     .split("</verdict>")[0]
@@ -371,11 +371,11 @@ def agent_inference(
                 )
                 if "Accept" in verdict:
                     assert not "Reject" in verdict
-                    print(f"Mask {i+1} accepted, keeping it in the outputs.")
+                    print(f"Mask {i + 1} accepted, keeping it in the outputs.")
                     masks_to_keep.append(i)
                 elif "Reject" in verdict:
                     assert not "Accept" in verdict
-                    print(f"Mask {i+1} rejected, removing it from the outputs.")
+                    print(f"Mask {i + 1} rejected, removing it from the outputs.")
                 else:
                     raise ValueError(
                         f"Unexpected verdict in generated text: {checking_generated_text}. Expected 'Accept' or 'Reject'."
@@ -397,7 +397,7 @@ def agent_inference(
                 sam_output_dir, rf"{LATEST_SAM3_TEXT_PROMPT}.png"
             ).replace(
                 ".png",
-                f"_selected_masks_{'-'.join(map(str, [i+1 for i in masks_to_keep]))}.png".replace(
+                f"_selected_masks_{'-'.join(map(str, [i + 1 for i in masks_to_keep]))}.png".replace(
                     "/", "_"
                 ),
             )

sam3/agent/client_sam3.py

@@ -7,7 +7,6 @@ import os
 
 import torch
 from PIL import Image
-
 from sam3.model.box_ops import box_xyxy_to_xywh
 from sam3.train.masks_ops import rle_encode
 

sam3/agent/helpers/boxes.py

@@ -84,9 +84,9 @@ class BoxMode(IntEnum):
         ], "Relative mode not yet supported!"
 
         if from_mode == BoxMode.XYWHA_ABS and to_mode == BoxMode.XYXY_ABS:
-            assert (
-                arr.shape[-1] == 5
-            ), "The last dimension of input shape must be 5 for XYWHA format"
+            assert arr.shape[-1] == 5, (
+                "The last dimension of input shape must be 5 for XYWHA format"
+            )
             original_dtype = arr.dtype
             arr = arr.double()
 
@@ -244,9 +244,9 @@ class Boxes:
         if isinstance(item, int):
             return Boxes(self.tensor[item].view(1, -1))
         b = self.tensor[item]
-        assert (
-            b.dim() == 2
-        ), "Indexing on Boxes with {} failed to return a matrix!".format(item)
+        assert b.dim() == 2, (
+            "Indexing on Boxes with {} failed to return a matrix!".format(item)
+        )
         return Boxes(b)
 
     def __len__(self) -> int:
@@ -425,7 +425,7 @@ def matched_pairwise_iou(boxes1: Boxes, boxes2: Boxes) -> torch.Tensor:
         Tensor: iou, sized [N].
     """
     assert len(boxes1) == len(boxes2), (
-        "boxlists should have the same" "number of entries, got {}, {}".format(
+        "boxlists should have the samenumber of entries, got {}, {}".format(
             len(boxes1), len(boxes2)
         )
     )

sam3/agent/helpers/masks.py

@@ -13,7 +13,6 @@ from torch import device
 
 from .boxes import Boxes
 from .memory import retry_if_cuda_oom
-
 from .roi_align import ROIAlign
 
 
@@ -142,10 +141,10 @@ class BitMasks:
         if isinstance(item, int):
             return BitMasks(self.tensor[item].unsqueeze(0))
         m = self.tensor[item]
-        assert (
-            m.dim() == 3
-        ), "Indexing on BitMasks with {} returns a tensor with shape {}!".format(
-            item, m.shape
+        assert m.dim() == 3, (
+            "Indexing on BitMasks with {} returns a tensor with shape {}!".format(
+                item, m.shape
+            )
         )
         return BitMasks(m)
 

sam3/agent/helpers/rotated_boxes.py

@@ -363,9 +363,9 @@ class RotatedBoxes(Boxes):
         if isinstance(item, int):
             return RotatedBoxes(self.tensor[item].view(1, -1))
         b = self.tensor[item]
-        assert (
-            b.dim() == 2
-        ), "Indexing on RotatedBoxes with {} failed to return a matrix!".format(item)
+        assert b.dim() == 2, (
+            "Indexing on RotatedBoxes with {} failed to return a matrix!".format(item)
+        )
         return RotatedBoxes(b)
 
     def __len__(self) -> int:

sam3/agent/helpers/visualizer.py

@@ -20,7 +20,6 @@ from matplotlib.backends.backend_agg import FigureCanvasAgg
 from PIL import Image
 
 from .boxes import Boxes, BoxMode
-
 from .color_map import random_color
 from .keypoints import Keypoints
 from .masks import BitMasks, PolygonMasks
@@ -222,9 +221,9 @@ class _PanopticPrediction:
                 empty_ids.append(id)
         if len(empty_ids) == 0:
             return np.zeros(self._seg.shape, dtype=np.uint8)
-        assert (
-            len(empty_ids) == 1
-        ), ">1 ids corresponds to no labels. This is currently not supported"
+        assert len(empty_ids) == 1, (
+            ">1 ids corresponds to no labels. This is currently not supported"
+        )
         return (self._seg != empty_ids[0]).numpy().astype(np.bool)
 
     def semantic_masks(self):

sam3/agent/inference.py

@@ -41,7 +41,7 @@ def run_single_image_inference(
         print(f"Output JSON {output_json_path} already exists. Skipping.")
         return
 
-    print(f"{'-'*30} Starting SAM 3 Agent Session... {'-'*30} ")
+    print(f"{'-' * 30} Starting SAM 3 Agent Session... {'-' * 30} ")
     agent_history, final_output_dict, rendered_final_output = agent_inference(
         image_path,
         text_prompt,
@@ -50,7 +50,7 @@ def run_single_image_inference(
         output_dir=output_dir,
         debug=debug,
     )
-    print(f"{'-'*30} End of SAM 3 Agent Session... {'-'*30} ")
+    print(f"{'-' * 30} End of SAM 3 Agent Session... {'-' * 30} ")
 
     final_output_dict["text_prompt"] = text_prompt
     final_output_dict["image_path"] = image_path

sam3/agent/viz.py

@@ -73,7 +73,9 @@ def visualize(
         idx = int(zoom_in_index)
         num_masks = len(input_json.get("pred_masks", []))
         if idx < 0 or idx >= num_masks:
-            raise ValueError(f"zoom_in_index {idx} is out of range (0..{num_masks-1}).")
+            raise ValueError(
+                f"zoom_in_index {idx} is out of range (0..{num_masks - 1})."
+            )
 
         # (1) Replicate zoom_in_and_visualize
         object_data = {