Delete Model.py

Model.py

@@ -1,117 +0,0 @@
-from transformers import PreTrainedModel
-import torch
-import os
-
-class InceptionV3ModelForImageClassification(PreTrainedModel):
-	def __init__(self, config):
-		super().__init__(config)
-
-		model_path = "pytorch_model.bin"
-
-		if self.config.model_name == "google-safesearch-mini":
-			if not os.path.exists(model_path):
-				import requests
-				url = "https://huggingface.co/FredZhang7/google-safesearch-mini/resolve/main/pytorch_model.bin"
-				r = requests.get(url, allow_redirects=True)
-				open(model_path, 'wb').write(r.content)
-			self.model = torch.jit.load(model_path)
-		else:
-			raise ValueError(f"Model {self.config.model_name} not found.")
-
-	def forward(self, input_ids):
-		return self.model(input_ids), None if self.config.model_name == "inception_v3" else self.model(input_ids)
-
-	def freeze(self):
-		for param in self.model.parameters():
-			param.requires_grad = False
-
-	def unfreeze(self):
-		for param in self.model.parameters():
-			param.requires_grad = True
-
-	def train(self, mode=True):
-		super().train(mode)
-		self.model.train(mode)
-
-	def eval(self):
-		return self.train(False)
-
-	def to(self, device):
-		self.model.to(device)
-		return self
-
-	def cuda(self, device=None):
-		return self.to("cuda")
-
-	def cpu(self):
-		return self.to("cpu")
-
-	def state_dict(self, destination=None, prefix='', keep_vars=False):
-		return self.model.state_dict(destination, prefix, keep_vars)
-
-	def load_state_dict(self, state_dict, strict=True):
-		return self.model.load_state_dict(state_dict, strict)
-
-	def parameters(self, recurse=True):
-		return self.model.parameters(recurse)
-
-	def named_parameters(self, prefix='', recurse=True):
-		return self.model.named_parameters(prefix, recurse)
-
-	def children(self):
-		return self.model.children()
-
-	def named_children(self):
-		return self.model.named_children()
-
-	def modules(self):
-		return self.model.modules()
-
-	def named_modules(self, memo=None, prefix=''):
-		return self.model.named_modules(memo, prefix)
-
-	def zero_grad(self, set_to_none=False):
-		return self.model.zero_grad(set_to_none)
-
-	def share_memory(self):
-		return self.model.share_memory()
-
-	def transform(self, image):
-		from torchvision import transforms
-		transform = transforms.Compose([
-			transforms.Resize(299),
-			transforms.ToTensor(),
-			transforms.Normalize(mean=self.config.mean, std=self.config.std)
-		])
-		image = transform(image)
-		return image
-
-	def open_image(self, path):
-		from PIL import Image
-		path = 'https://images.unsplash.com/photo-1594568284297-7c64464062b1'
-		if path.startswith('http://') or path.startswith('https://'):
-			import requests
-			from io import BytesIO
-			response = requests.get(path)
-			image = Image.open(BytesIO(response.content)).convert('RGB')
-		else:
-			image = Image.open(path).convert('RGB')
-		return image
-
-	def predict(self, path, device="cuda", print_tensor=True):
-		image = self.open_image(path)
-		image = self.transform(image)
-		image = image.unsqueeze(0)
-		self.eval()
-		if device == "cuda":
-			image = image.cuda()
-			self.cuda()
-		else:
-			image = image.cpu()
-			self.cpu()
-		with torch.no_grad():
-			out, aux = self(image)
-			if print_tensor:
-				print(out)
-			_, predicted = torch.max(out.logits, 1)
-		return self.config.classes[str(predicted.item())]