Upload WGAN_GP

config.json

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+{
+  "architectures": [
+    "WGAN_GP"
+  ],
+  "auto_map": {
+    "AutoConfig": "config.Config",
+    "AutoModel": "model.WGAN_GP"
+  },
+  "batch_size": 256,
+  "cfg": {
+    "batch_size": 256,
+    "comet_project": "wgan-gp",
+    "comet_workspace": "wgan",
+    "d_iter_per_g": 5,
+    "data_dir": "/home/user/PyTorch-Lightning-GAN/GAN/celeba",
+    "device": "cuda",
+    "epochs": 500,
+    "final_activation": {
+      "g": "tanh"
+    },
+    "images_dir": "images",
+    "img_ch": 3,
+    "imsize": 64,
+    "lr": {
+      "d": 0.0001,
+      "g": 0.0001
+    },
+    "name": "WGAN-GP",
+    "norm_type": {
+      "d": "GroupNorm",
+      "g": "GroupNorm"
+    },
+    "num_sample_epochs": 20,
+    "num_workers": 4,
+    "resume": false,
+    "samples_dir": "samples",
+    "save_image_freq": 50,
+    "save_model_freq": 100,
+    "seed": 42,
+    "use_fabric": false,
+    "w_gp": 10,
+    "weights_dir": "chkpt",
+    "zdim": 128
+  },
+  "comet_project": "wgan-gp",
+  "comet_workspace": "wgan",
+  "d_iter_per_g": 5,
+  "data_dir": "/home/user/PyTorch-Lightning-GAN/GAN/celeba",
+  "device": "cuda",
+  "epochs": 500,
+  "final_activation": {
+    "g": "tanh"
+  },
+  "images_dir": "images",
+  "img_ch": 3,
+  "imsize": 64,
+  "lr": {
+    "d": 0.0001,
+    "g": 0.0001
+  },
+  "model_type": "WGAN_GP",
+  "name": "WGAN-GP",
+  "norm_type": {
+    "d": "GroupNorm",
+    "g": "GroupNorm"
+  },
+  "num_sample_epochs": 20,
+  "num_workers": 4,
+  "resume": false,
+  "samples_dir": "samples",
+  "save_image_freq": 50,
+  "save_model_freq": 100,
+  "seed": 42,
+  "torch_dtype": "float32",
+  "transformers_version": "4.35.2",
+  "use_fabric": false,
+  "w_gp": 10,
+  "weights_dir": "chkpt",
+  "zdim": 128
+}

config.py

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+from transformers import PretrainedConfig
+class Config(PretrainedConfig):
+    model_type = "WGAN_GP"
+    def __init__(self, **kwargs):
+        super().__init__(**kwargs)
+        self.cfg=kwargs

model.py

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+import huggingface_hub
+from .config import Config
+from transformers import PreTrainedModel
+from dcgan import Generator
+
+# config = Config()
+# config.save_pretrained("WGAN-GP")
+class WGAN_GP(PreTrainedModel):
+    config_class = Config
+    def __init__(self, config):
+        super().__init__(config)
+        
+        self.generator=Generator(config.cfg["imsize"],config.cfg["img_ch"],config.cfg["zdim"],
+           config.cfg["norm_type"]["g"],config.cfg["final_activation"]["g"])
+
+    def forward(self, input):
+        return self.generator(input)

model.safetensors

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+version https://git-lfs.github.com/spec/v1
+oid sha256:06bd22721a4ef7a2db42ec94c3a9b5fca1aca5bbd7352502d3e6617e2e84639f
+size 52470080