add passt-L pre-trained weights

hear21passt/models/passt.py

@@ -169,6 +169,10 @@ def _cfg(url='', **kwargs):
         url='https://github.com/kkoutini/PaSST/releases/download/v.0.0.9/passt-s-kd-ap.486.pt',
         mean=IMAGENET_DEFAULT_MEAN, std=IMAGENET_DEFAULT_STD, input_size=(1, 128, 998), crop_pct=1.0,
         classifier=('head.1', 'head_dist'), num_classes=527),
+    'passt_l_kd_p16_128_ap47': _cfg(
+        url='https://github.com/kkoutini/PaSST/releases/download/v.0.0.10/passt-l-kd-ap.47.pt',
+        mean=IMAGENET_DEFAULT_MEAN, std=IMAGENET_DEFAULT_STD, input_size=(1, 128, 998), crop_pct=1.0,
+        classifier=('head.1', 'head_dist'), num_classes=527),
     'passt_s_p16_s16_128_ap468': _cfg(
         url='https://github.com/kkoutini/PaSST/releases/download/v0.0.2-audioset/passt-s-f128-p16-s16-ap.468.pt',
         mean=IMAGENET_DEFAULT_MEAN, std=IMAGENET_DEFAULT_STD, input_size=(1, 128, 998), crop_pct=1.0,
@@ -726,6 +730,18 @@ def passt_s_kd_p16_128_ap486(pretrained=False, **kwargs):
         'passt_s_kd_p16_128_ap486', pretrained=pretrained, distilled=True, **model_kwargs)
     return model
 
+def passt_l_kd_p16_128_ap47(pretrained=False, **kwargs):
+    """ PaSST pre-trained on AudioSet
+    """
+    print("\n\n Loading PaSST-L (light, reduced depth=7) pre-trained on AudioSet (with KD) Patch 16 stride 10 structured patchout mAP=4708 \n\n")
+    model_kwargs = dict(patch_size=16, embed_dim=768, depth=7, num_heads=12, **kwargs)
+    if model_kwargs.get("stride") != (10, 10):
+        warnings.warn(
+            f"This model was pre-trained with strides {(10, 10)}, but now you set (fstride,tstride) to {model_kwargs.get('stride')}.")
+    model = _create_vision_transformer(
+        'passt_l_kd_p16_128_ap47', pretrained=pretrained, distilled=True, **model_kwargs)
+    return model
+
 def passt_s_p16_s16_128_ap468(pretrained=False, **kwargs):
     """ PaSST pre-trained on AudioSet
     """
@@ -866,6 +882,8 @@ def get_model(arch="passt_s_kd_p16_128_ap486", pretrained=True, n_classes=527, i
         model_func = passt_s_swa_p16_128_ap476
     elif arch == "passt_s_kd_p16_128_ap486":  # pretrained
         model_func = passt_s_kd_p16_128_ap486
+    elif arch == "passt_l_kd_p16_128_ap47":  # pretrained passt-L
+        model_func = passt_l_kd_p16_128_ap47
     elif arch == "passt_s_p16_s16_128_ap468":
         if fstride!=16 or tstride!=16:
             raise ValueError("fstride and tstride must be 16 for arch=passt_s_p16_s16_128_ap468. "

test.py

@@ -31,3 +31,5 @@
     model.net = get_model_passt("stfthop160", input_tdim=2000)
     model.net = get_model_passt("passt_20sec", input_tdim=2000)
     model.net = get_model_passt("passt_30sec", input_tdim=3000)
+
+    model.net = get_model_passt("passt_l_kd_p16_128_ap47")