changed model weight s3 paths to new brain-score AWS bucket

brainscore_vision/models/cornet_s/model.py

@@ -33,9 +33,9 @@ def __init__(self, model):
     model_ctr = getattr(mod, 'CORnet_S')
     model = model_ctr()
     model = Wrapper(model)  # model was wrapped with DataParallel, so weights require `module.` prefix
-    weights_path = load_weight_file(bucket="brainscore-vision", folder_name="models",
+    weights_path = load_weight_file(bucket="brainscore-storage", folder_name="brainscore-vision/models",
                                     relative_path="cornet_s/cornet_s_epoch43.pth.tar",
-                                    version_id="4EAQnCqTy.2MCKiXTJ4l02iG8l3e.yfQ",
+                                    version_id="null",
                                     sha1="a4bfd8eda33b45fd945da1b972ab0b7cad38d60f")
     checkpoint = torch.load(weights_path, map_location=lambda storage, loc: storage)  # map onto cpu
     model.load_state_dict(checkpoint['state_dict'])

brainscore_vision/models/custom_model_cv_18_dagger_408/model.py

@@ -47,9 +47,9 @@ def load_preprocess_custom_model(image_filepaths, image_size, **kwargs):
 def get_model(name):
     assert name == 'custom_model_cv_18_dagger_408'
     model = create_model('crossvit_18_dagger_408', pretrained=False)
-    weights_path = load_weight_file(bucket="brainscore-vision", folder_name="models",
+    weights_path = load_weight_file(bucket="brainscore-storage", folder_name="brainscore-vision/models",
                                     relative_path="custom_model_cv_18_dagger_408/crossvit_18_dagger_408_adv_finetuned_epoch5.pt",
-                                    version_id="pQVPFC_iiWpRRr7P54qxQfRzjNSn2uYB",
+                                    version_id="null",
                                     sha1="c769518485e352d5a2e6f3e588d6208cbad71b69")
     checkpoint = torch.load(weights_path, map_location=torch.device('cpu'))
     model.load_state_dict(checkpoint['state_dict'], strict=True)

brainscore_vision/models/effnetb1_cutmix_augmix_sam_e1_5avg_424x377/model.py

@@ -65,9 +65,9 @@ def get_model(name):
     assert name == 'effnetb1_cutmix_augmix_sam_e1_5avg_424x377'
     model_tf_efficientnet_b1_ns = EffNetBX()
 
-    weights_path = load_weight_file(bucket="brainscore-vision", folder_name="models",
+    weights_path = load_weight_file(bucket="brainscore-storage", folder_name="brainscore-vision/models",
                                     relative_path="effnetb1_cutmix_augmix_sam_e1_5avg_424x377/weights1_5_avg.pth",
-                                    version_id="EqB6P7BittVdkgRd3oMncq_j9AAdiYvz",
+                                    version_id="null",
                                     sha1="871bd10e6ce164bfe8f3ce10bb77a69d326d7b65")
     model_tf_efficientnet_b1_ns.load_state_dict(torch.load(weights_path, map_location=torch.device('cpu'))["model"])
     model = model_tf_efficientnet_b1_ns.efnet_model

brainscore_vision/models/effnetb1_cutmixpatch_augmix_robust32_avge4e7_manylayers_324x288/model.py

@@ -70,9 +70,9 @@ def forward(self, x):
 def get_model(name):
     assert name == 'effnetb1_cutmixpatch_augmix_robust32_avge4e7_manylayers_324x288'
     model_tf_efficientnet_b1_ns = EffNetBX()
-    weights_path = load_weight_file(bucket="brainscore-vision", folder_name="models",
+    weights_path = load_weight_file(bucket="brainscore-storage", folder_name="brainscore-vision/models",
                                     relative_path="effnetb1_cutmixpatch_augmix_robust32_avge4e7_manylayers_324x288/tf_efficientnet_b1_ns_robust_cutmixpatchresize_augmix_e4toe7.pth",
-                                    version_id="iB0UqbguDpYHD0HRbMt1F1er3c414yWr",
+                                    version_id="null",
                                     sha1="37f3ac1b14e80cfaa99fa5f412c1e132480ed5b6")
 
     model_tf_efficientnet_b1_ns.load_state_dict(torch.load(weights_path,map_location=torch.device('cpu'))["model"])

brainscore_vision/models/hmax/model.py

@@ -18,9 +18,9 @@ def get_model(name):
 
 
 def get_hmax(identifier, image_size):
-    weights_path = load_weight_file(bucket="brainscore-vision", folder_name="models",
+    weights_path = load_weight_file(bucket="brainscore-storage", folder_name="brainscore-vision/models",
                                     relative_path="hmax/universal_patch_set.mat",
-                                    version_id="fIX.lsvnc8qqjDr_sG_Dl9RyqWuG0OGC",
+                                    version_id="null",
                                     sha1="acc7316fcb0d1797486bb62753b71e158216a92a")
     global model 
     model = HMAX(str(weights_path))

brainscore_vision/models/resnet_50_robust/model.py

@@ -16,9 +16,9 @@ def get_model(name):
     model_ctr = getattr(module, 'resnet50')
     model = model_ctr()
     preprocessing = functools.partial(load_preprocess_images, image_size=224)
-    weights_path = load_weight_file(bucket="brainscore-vision", folder_name="models",
+    weights_path = load_weight_file(bucket="brainscore-storage", folder_name="brainscore-vision/models",
                                     relative_path="resnet-50-robust/ImageNet.pt",
-                                    version_id=".shHB0L_L9L3Mtco0Kf4EBP3Xj9nLKnC",
+                                    version_id="null",
                                     sha1="cc6e4441abc8ad6d2f4da5db84836e544bfb53fd")
     checkpoint = torch.load(weights_path, map_location=torch.device('cpu'))
 

brainscore_vision/models/voneresnet_50_non_stochastic/model.py

@@ -24,9 +24,9 @@ def get_model_from_s3():
     model_arch = 'resnet50'
     pretrained = True
     if pretrained and model_arch:
-        weights_path = load_weight_file(bucket="brainscore-vision", folder_name="models",
+        weights_path = load_weight_file(bucket="brainscore-storage", folder_name="brainscore-vision/models",
                                         relative_path="voneresnet-50-non_stochastic/voneresnet50_ns_e70.pth.tar",
-                                        version_id="vDk2cwi2xjwGqhGyyjp8lEGSfcaFzB61",
+                                        version_id="null",
                                         sha1="c270528818d6d7fc67a6aec86919d47311ad6221")
         ckpt_data = torch.load(weights_path, map_location=torch.device('cpu'))
         stride = ckpt_data['flags']['stride']