Merge branch 'main' into vision_conda_forge

Author: NicolasHug

android/build.gradle

@@ -15,13 +15,12 @@ allprojects {
             androidSupportAppCompatV7Version = "28.0.0"
             fbjniJavaOnlyVersion = "0.0.3"
             soLoaderNativeLoaderVersion = "0.8.0"
-            pytorchAndroidVersion = "1.10.0"
+            pytorchAndroidVersion = "1.12"
         }
 
         repositories {
             google()
             mavenCentral()
-            jcenter()
         }
 
         dependencies {
@@ -32,7 +31,7 @@ allprojects {
 
     repositories {
         google()
-        jcenter()
+        mavenCentral()
     }
 }
 

android/ops/build.gradle

@@ -2,7 +2,6 @@ apply plugin: 'com.android.library'
 apply plugin: 'maven'
 
 repositories {
-  jcenter()
   mavenCentral()
   maven {
     url "https://oss.sonatype.org/content/repositories/snapshots"

android/test_app/app/build.gradle

@@ -1,7 +1,6 @@
 apply plugin: 'com.android.application'
 
 repositories {
-    jcenter()
     mavenCentral()
     maven {
         url "https://oss.sonatype.org/content/repositories/snapshots"

docs/Makefile

@@ -6,9 +6,7 @@ ifneq ($(EXAMPLES_PATTERN),)
 endif
 
 # You can set these variables from the command line.
-# TODO: Once the models doc revamp is done, set back the -W option to raise
-# errors on warnings. See https://github.com/pytorch/vision/pull/5821#discussion_r850500693
-SPHINXOPTS    = -j auto $(EXAMPLES_PATTERN_OPTS)
+SPHINXOPTS    = -W -j auto $(EXAMPLES_PATTERN_OPTS)
 SPHINXBUILD   = sphinx-build
 SPHINXPROJ    = torchvision
 SOURCEDIR     = source

docs/source/conf.py

@@ -316,6 +316,11 @@ def inject_weight_metadata(app, what, name, obj, options, lines):
     """
 
     if obj.__name__.endswith(("_Weights", "_QuantizedWeights")):
+
+        if len(obj) == 0:
+            lines[:] = ["There are no available pre-trained weights."]
+            return
+
         lines[:] = [
             "The model builder above accepts the following values as the ``weights`` parameter.",
             f"``{obj.__name__}.DEFAULT`` is equivalent to ``{obj.DEFAULT}``.",
@@ -329,37 +334,44 @@ 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"This weight is also available as ``{obj.__name__}.DEFAULT``.", ""]
+                lines += [f"Also available as ``{obj.__name__}.DEFAULT``."]
+            lines += [""]
 
             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)))
 
-            # 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)
-
-            meta_with_metrics.pop("categories", None)  # We don't want to document these, they can be too long
-
-            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 == "recipe":
+            for k, v in meta.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
             lines += [".. table::", ""]
             lines += textwrap.indent(table, " " * 4).split("\n")
             lines.append("")
+            lines.append(
+                f"The inference transforms are available at ``{str(field)}.transforms`` and "
+                f"perform the following preprocessing operations: {field.transforms().describe()}"
+            )
+            lines.append("")
 
 
-def generate_weights_table(module, table_name, metrics, include_patterns=None, exclude_patterns=None):
+def generate_weights_table(module, table_name, metrics, dataset, 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]
@@ -376,7 +388,7 @@ def generate_weights_table(module, table_name, metrics, include_patterns=None, e
     content = [
         (
             f":class:`{w} <{type(w).__name__}>`",
-            *(w.meta["metrics"][metric] for metric in metrics_keys),
+            *(w.meta["_metrics"][dataset][metric] for metric in metrics_keys),
             f"{w.meta['num_params']/1e6:.1f}M",
             f"`link <{w.meta['recipe']}>`__",
         )
@@ -393,29 +405,45 @@ def generate_weights_table(module, table_name, metrics, include_patterns=None, e
         table_file.write(f"{textwrap.indent(table, ' ' * 4)}\n\n")
 
 
-generate_weights_table(module=M, table_name="classification", metrics=[("acc@1", "Acc@1"), ("acc@5", "Acc@5")])
 generate_weights_table(
-    module=M.quantization, table_name="classification_quant", metrics=[("acc@1", "Acc@1"), ("acc@5", "Acc@5")]
+    module=M, table_name="classification", metrics=[("acc@1", "Acc@1"), ("acc@5", "Acc@5")], dataset="ImageNet-1K"
 )
 generate_weights_table(
-    module=M.detection, table_name="detection", metrics=[("box_map", "Box MAP")], exclude_patterns=["Mask", "Keypoint"]
+    module=M.quantization,
+    table_name="classification_quant",
+    metrics=[("acc@1", "Acc@1"), ("acc@5", "Acc@5")],
+    dataset="ImageNet-1K",
+)
+generate_weights_table(
+    module=M.detection,
+    table_name="detection",
+    metrics=[("box_map", "Box MAP")],
+    exclude_patterns=["Mask", "Keypoint"],
+    dataset="COCO-val2017",
 )
 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")]
+    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"
 )
-generate_weights_table(module=M.video, table_name="video", metrics=[("acc@1", "Acc@1"), ("acc@5", "Acc@5")])
 
 
 def setup(app):

docs/source/docutils.conf

@@ -1,2 +1,3 @@
+# Necessary for the table generated by autosummary to look decent
 [html writers]
-table_style: colwidths-auto  # Necessary for the table generated by autosummary to look decent
+table_style: colwidths-auto

docs/source/index.rst

@@ -38,7 +38,6 @@ architectures, and common image transformations for computer vision.
    ops
    io
    feature_extraction
-   models_new
 
 .. toctree::
    :maxdepth: 1