Upload ReconResNet

ReconResNet.py

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+from transformers import PreTrainedModel
+from ReconResNetBase import ReconResNetBase
+from ReconResNetConfig import ReconResNetConfig
+
+class ReconResNet(PreTrainedModel):
+    config_class = ReconResNetConfig
+    def __init__(self, config):
+        super().__init__(config)
+        self.model = ReconResNetBase(
+            in_channels=config.in_channels,
+            out_channels=config.out_channels,
+            res_blocks=config.res_blocks,
+            starting_nfeatures=config.starting_nfeatures,
+            updown_blocks=config.updown_blocks,
+            is_relu_leaky=config.is_relu_leaky,
+            do_batchnorm=config.do_batchnorm,
+            res_drop_prob=config.res_drop_prob,
+            is_replicatepad=config.is_replicatepad,
+            out_act=config.out_act,
+            forwardV=config.forwardV,
+            upinterp_algo=config.upinterp_algo,
+            post_interp_convtrans=config.post_interp_convtrans,
+            is3D=config.is3D)
+    def forward(self, x):
+        return self.model(x)

ReconResNetConfig.py

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+from transformers import PretrainedConfig
+from typing import List
+
+class ReconResNetConfig(PretrainedConfig):
+    model_type = "ReconResNet"
+    def __init__(
+            self,
+            in_channels=1, 
+            out_channels=1, 
+            res_blocks=14, 
+            starting_nfeatures=64, 
+            updown_blocks=2, 
+            is_relu_leaky=True, 
+            do_batchnorm=False, 
+            res_drop_prob=0.2,
+            is_replicatepad=0, 
+            out_act="sigmoid", 
+            forwardV=0, 
+            upinterp_algo='convtrans', 
+            post_interp_convtrans=False, 
+            is3D=False,
+            **kwargs):
+        self.in_channels = in_channels
+        self.out_channels = out_channels
+        self.res_blocks = res_blocks
+        self.starting_nfeatures = starting_nfeatures
+        self.updown_blocks = updown_blocks
+        self.is_relu_leaky = is_relu_leaky
+        self.do_batchnorm = do_batchnorm
+        self.res_drop_prob = res_drop_prob
+        self.is_replicatepad = is_replicatepad
+        self.out_act = out_act
+        self.forwardV = forwardV
+        self.upinterp_algo = upinterp_algo
+        self.post_interp_convtrans = post_interp_convtrans
+        self.is3D = is3D
+        super().__init__(**kwargs)

config.json

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+{
+  "architectures": [
+    "ReconResNet"
+  ],
+  "auto_map": {
+    "AutoConfig": "ReconResNetConfig.ReconResNetConfig",
+    "AutoModel": "ReconResNet.ReconResNet"
+  },
+  "do_batchnorm": false,
+  "forwardV": 0,
+  "in_channels": 1,
+  "is3D": false,
+  "is_relu_leaky": true,
+  "is_replicatepad": 0,
+  "model_type": "ReconResNet",
+  "out_act": "sigmoid",
+  "out_channels": 1,
+  "post_interp_convtrans": false,
+  "res_blocks": 14,
+  "res_drop_prob": 0.2,
+  "starting_nfeatures": 64,
+  "torch_dtype": "float32",
+  "transformers_version": "4.44.2",
+  "updown_blocks": 2,
+  "upinterp_algo": "convtrans"
+}

model.safetensors

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+version https://git-lfs.github.com/spec/v1
+oid sha256:b30255b8ecb23abc738ad0cf27e1bcbe469844f876611e2f2ac69b5600d02151
+size 69075000