fix build docs job

Author: abhi-glitchhg

docs/source/conf.py

@@ -103,7 +103,7 @@
 #
 # This is also used if you do content translation via gettext catalogs.
 # Usually you set "language" from the command line for these cases.
-language = "en"
+language = None
 
 # List of patterns, relative to source directory, that match files and
 # directories to ignore when looking for source files.
@@ -334,30 +334,32 @@ def inject_weight_metadata(app, what, name, obj, options, lines):
         lines.append("")
 
         for field in obj:
-            meta = copy(field.meta)
-
             lines += [f"**{str(field)}**:", ""]
-            lines += [meta.pop("_docs")]
-
             if field == obj.DEFAULT:
-                lines += [f"Also available as ``{obj.__name__}.DEFAULT``."]
-            lines += [""]
+                lines += [f"This weight is also available as ``{obj.__name__}.DEFAULT``.", ""]
 
             table = []
-            metrics = meta.pop("_metrics")
-            for dataset, dataset_metrics in metrics.items():
-                for metric_name, metric_value in dataset_metrics.items():
-                    table.append((f"{metric_name} (on {dataset})", str(metric_value)))
 
-            for k, v in meta.items():
+            # the `meta` dict contains another embedded `metrics` dict. To
+            # simplify the table generation below, we create the
+            # `meta_with_metrics` dict, where the metrics dict has been "flattened"
+            meta = copy(field.meta)
+            metrics = meta.pop("metrics", {})
+            meta_with_metrics = dict(meta, **metrics)
+
+            # We don't want to document these, they can be too long
+            for k in ["categories", "keypoint_names"]:
+                meta_with_metrics.pop(k, None)
+
+            custom_docs = meta_with_metrics.pop("_docs", None)  # Custom per-Weights docs
+            if custom_docs is not None:
+                lines += [custom_docs, ""]
+
+            for k, v in meta_with_metrics.items():
                 if k in {"recipe", "license"}:
                     v = f"`link <{v}>`__"
                 elif k == "min_size":
                     v = f"height={v[0]}, width={v[1]}"
-                elif k in {"categories", "keypoint_names"} and isinstance(v, list):
-                    max_visible = 3
-                    v_sample = ", ".join(v[:max_visible])
-                    v = f"{v_sample}, ... ({len(v)-max_visible} omitted)" if len(v) > max_visible else v_sample
                 table.append((str(k), str(v)))
             table = tabulate(table, tablefmt="rst")
             lines += [".. rst-class:: table-weights"]  # Custom CSS class, see custom_torchvision.css
@@ -366,12 +368,12 @@ def inject_weight_metadata(app, what, name, obj, options, lines):
             lines.append("")
             lines.append(
                 f"The inference transforms are available at ``{str(field)}.transforms`` and "
-                f"perform the following preprocessing operations: {field.transforms().describe()}"
+                f"perform the following operations: {field.transforms().describe()}"
             )
             lines.append("")
 
 
-def generate_weights_table(module, table_name, metrics, dataset, include_patterns=None, exclude_patterns=None):
+def generate_weights_table(module, table_name, metrics, include_patterns=None, exclude_patterns=None):
     weights_endswith = "_QuantizedWeights" if module.__name__.split(".")[-1] == "quantization" else "_Weights"
     weight_enums = [getattr(module, name) for name in dir(module) if name.endswith(weights_endswith)]
     weights = [w for weight_enum in weight_enums for w in weight_enum]
@@ -388,7 +390,7 @@ def generate_weights_table(module, table_name, metrics, dataset, include_pattern
     content = [
         (
             f":class:`{w} <{type(w).__name__}>`",
-            *(w.meta["_metrics"][dataset][metric] for metric in metrics_keys),
+            *(w.meta["metrics"][metric] for metric in metrics_keys),
             f"{w.meta['num_params']/1e6:.1f}M",
             f"`link <{w.meta['recipe']}>`__",
         )
@@ -403,48 +405,32 @@ def generate_weights_table(module, table_name, metrics, dataset, include_pattern
         table_file.write(".. table::\n")
         table_file.write(f"    :widths: 100 {'20 ' * len(metrics_names)} 20 10\n\n")
         table_file.write(f"{textwrap.indent(table, ' ' * 4)}\n\n")
-        table_file.write(f"{table_name} Weights Table \n{2*len(table_name)*'*'}\n")
+        table_file.write(f"{table_name} Weights Table \n{4*len(table_name)*'*'}\n")
 
 
+generate_weights_table(module=M, table_name="classification", metrics=[("acc@1", "Acc@1"), ("acc@5", "Acc@5")])
 generate_weights_table(
-    module=M, table_name="classification", metrics=[("acc@1", "Acc@1"), ("acc@5", "Acc@5")], dataset="ImageNet-1K"
-)
-generate_weights_table(
-    module=M.quantization,
-    table_name="classification_quant",
-    metrics=[("acc@1", "Acc@1"), ("acc@5", "Acc@5")],
-    dataset="ImageNet-1K",
+    module=M.quantization, table_name="classification_quant", metrics=[("acc@1", "Acc@1"), ("acc@5", "Acc@5")]
 )
 generate_weights_table(
-    module=M.detection,
-    table_name="detection",
-    metrics=[("box_map", "Box MAP")],
-    exclude_patterns=["Mask", "Keypoint"],
-    dataset="COCO-val2017",
+    module=M.detection, table_name="detection", metrics=[("box_map", "Box MAP")], exclude_patterns=["Mask", "Keypoint"]
 )
 generate_weights_table(
     module=M.detection,
     table_name="instance_segmentation",
     metrics=[("box_map", "Box MAP"), ("mask_map", "Mask MAP")],
-    dataset="COCO-val2017",
     include_patterns=["Mask"],
 )
 generate_weights_table(
     module=M.detection,
     table_name="detection_keypoint",
     metrics=[("box_map", "Box MAP"), ("kp_map", "Keypoint MAP")],
-    dataset="COCO-val2017",
     include_patterns=["Keypoint"],
 )
 generate_weights_table(
-    module=M.segmentation,
-    table_name="segmentation",
-    metrics=[("miou", "Mean IoU"), ("pixel_acc", "pixelwise Acc")],
-    dataset="COCO-val2017-VOC-labels",
-)
-generate_weights_table(
-    module=M.video, table_name="video", metrics=[("acc@1", "Acc@1"), ("acc@5", "Acc@5")], dataset="Kinetics-400"
+    module=M.segmentation, table_name="segmentation", metrics=[("miou", "Mean IoU"), ("pixel_acc", "pixelwise Acc")]
 )
+generate_weights_table(module=M.video, table_name="video", metrics=[("acc@1", "Acc@1"), ("acc@5", "Acc@5")])
 
 
 def setup(app):