Add at new repo again

vton-api/preprocess/humanparsing/mhp_extension/detectron2/tools/convert-torchvision-to-d2.py

@@ -0,0 +1,56 @@
+#!/usr/bin/env python
+# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
+
+import pickle as pkl
+import sys
+import torch
+
+"""
+Usage:
+  # download one of the ResNet{18,34,50,101,152} models from torchvision:
+  wget https://download.pytorch.org/models/resnet50-19c8e357.pth -O r50.pth
+  # run the conversion
+  ./convert-torchvision-to-d2.py r50.pth r50.pkl
+
+  # Then, use r50.pkl with the following changes in config:
+
+MODEL:
+  WEIGHTS: "/path/to/r50.pkl"
+  PIXEL_MEAN: [123.675, 116.280, 103.530]
+  PIXEL_STD: [58.395, 57.120, 57.375]
+  RESNETS:
+    DEPTH: 50
+    STRIDE_IN_1X1: False
+INPUT:
+  FORMAT: "RGB"
+
+  These models typically produce slightly worse results than the
+  pre-trained ResNets we use in official configs, which are the
+  original ResNet models released by MSRA.
+"""
+
+if __name__ == "__main__":
+    input = sys.argv[1]
+
+    obj = torch.load(input, map_location="cpu")
+
+    newmodel = {}
+    for k in list(obj.keys()):
+        old_k = k
+        if "layer" not in k:
+            k = "stem." + k
+        for t in [1, 2, 3, 4]:
+            k = k.replace("layer{}".format(t), "res{}".format(t + 1))
+        for t in [1, 2, 3]:
+            k = k.replace("bn{}".format(t), "conv{}.norm".format(t))
+        k = k.replace("downsample.0", "shortcut")
+        k = k.replace("downsample.1", "shortcut.norm")
+        print(old_k, "->", k)
+        newmodel[k] = obj.pop(old_k).detach().numpy()
+
+    res = {"model": newmodel, "__author__": "torchvision", "matching_heuristics": True}
+
+    with open(sys.argv[2], "wb") as f:
+        pkl.dump(res, f)
+    if obj:
+        print("Unconverted keys:", obj.keys())