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app-multiple-inp-out.py

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+import gradio as gr
+import torch
+import torchvision
+from PIL import Image
+from torchvision import models
+from torch import nn 
+from typing import List
+import json
+
+#Read labels file1
+with open('cat_to_name.json','r') as f:
+    cat_to_name = json.load(f)
+
+#Update last layer of model
+def set_parameter_requires_grad(model, feature_extracting):
+    if feature_extracting:
+        for param in model.parameters():
+            param.requires_grad = False
+
+def update_last_layer_pretrained_model(pretrained_model, num_classes, feature_extract):
+    set_parameter_requires_grad(pretrained_model, feature_extract)
+    if hasattr(pretrained_model, 'fc') and 'resnet' in pretrained_model.__class__.__name__.lower(): #resnet
+        num_ftrs = pretrained_model.fc.in_features
+        pretrained_model.fc = nn.Linear(num_ftrs, num_classes, bias = True)
+    elif hasattr(pretrained_model, 'classifier') and ('alexnet' in pretrained_model.__class__.__name__.lower() or 'vgg' in pretrained_model.__class__.__name__.lower()): #alexNet, vgg
+        num_ftrs = pretrained_model.classifier[6].in_features
+        pretrained_model.classifier[6] = nn.Linear(num_ftrs, num_classes, bias = True)
+    elif hasattr(pretrained_model, 'classifier') and 'squeezenet' in pretrained_model.__class__.__name__.lower(): #squeezenet
+        pretrained_model.classifier[1] = nn.Conv2d(512, num_classes, kernel_size=(1,1), stride=(1,1))
+        pretrained_model.num_classes = num_classes
+    elif hasattr(pretrained_model, 'classifier') and ('efficientnet' in pretrained_model.__class__.__name__.lower() or 'mobilenet' in pretrained_model.__class__.__name__.lower()): #efficientnet, mobilenet
+        num_ftrs = pretrained_model.classifier[1].in_features
+        pretrained_model.classifier[1] = nn.Linear(num_ftrs, num_classes, bias = True)
+    elif hasattr(pretrained_model, 'AuxLogits') and 'inception' in pretrained_model.__class__.__name__.lower(): #inception
+        num_ftrs = pretrained_model.AuxLogits.fc.in_features 
+        pretrained_model.AuxLogits.fc = nn.Linear(num_ftrs, num_classes) #Auxilary net
+        num_ftrs = pretrained_model.fc.in_features
+        pretrained_model.fc = nn.Linear(num_ftrs,num_classes) #Primary net
+    elif hasattr(pretrained_model, 'classifier') and 'densenet' in pretrained_model.__class__.__name__.lower(): #densenet
+        num_ftrs = pretrained_model.classifier.in_features
+        pretrained_model.classifier = nn.Linear(num_ftrs, num_classes, bias = True)
+    elif hasattr(pretrained_model, 'heads') and 'visiontransformer' in pretrained_model.__class__.__name__.lower(): #vit transformer
+        num_ftrs = pretrained_model.heads.head.in_features
+        pretrained_model.heads.head = nn.Linear(num_ftrs, num_classes, bias = True)
+    elif hasattr(pretrained_model, 'head') and 'swin' in pretrained_model.__class__.__name__.lower(): #swin transformer
+        num_ftrs = pretrained_model.head.in_features
+        pretrained_model.head = nn.Linear(num_ftrs, num_classes, bias = True)
+    return pretrained_model        
+
+#pred_image
+def pred_image(model, image_path, class_names = None, transform=None, device: torch.device = "cuda" if torch.cuda.is_available() else "cpu"):
+
+    target_image = Image.open(image_path)
+    if transform:
+        target_image = transform(target_image)
+    model.to(device)
+    model.eval()
+    with torch.inference_mode():
+        target_image = target_image.unsqueeze(dim=0)
+        target_image_pred = model(target_image.to(device))
+
+    target_image_pred_probs = torch.softmax(target_image_pred, dim=1)
+    ps = target_image_pred_probs.topk(3)
+    ps_numpy = ps[0].cpu().numpy()[0]
+    idxs = [class_names[i] for i in ps[1].numpy()[0]] if class_names else ps[1].numpy()[0]
+
+    return (ps_numpy, idxs)
+
+def process_input(image_path):
+
+    #Load Model
+    model_name, model_weights, model_path = ('efficientnet_b2','EfficientNet_B2_Weights','flowers_efficientnet_b2_model.pth')
+    #model_name, model_weights, model_path = ('alexnet','AlexNet_Weights','flowers_alexnet_model.pth')
+    checkpoint = torch.load(model_path, map_location='cpu')
+    pretrained_weights = eval(f"models.{model_weights}.DEFAULT")
+    auto_transforms = pretrained_weights.transforms()
+    #pretrained_model = eval(f"torchvision.models.{model_name}(weights = pretrained_weights)")
+    pretrained_model = eval(f"models.{model_name}(pretrained = True)")
+    pretrained_model = update_last_layer_pretrained_model(pretrained_model, 102, True)
+    pretrained_model.class_to_idx = checkpoint['class_to_idx']
+    pretrained_model.class_names = checkpoint['class_names']    
+    pretrained_model.load_state_dict(checkpoint['state_dict'])
+    pretrained_model.to('cpu')
+        
+    #Predict
+    #image_path = 'which-flower/80_image_02020.jpg'
+    probs, idxs = pred_image(model=pretrained_model, image_path=image_path, class_names=pretrained_model.class_names, transform=auto_transforms)
+    names = [cat_to_name[i] for i in idxs]
+    
+    #Display or return to main function
+    print({names[i]: float(probs[i]) for i in range(len(names))})
+    return {names[i]: float(probs[i]) for i in range(len(names))}, {names[i]: float(probs[i]) for i in range(len(names))}
+    #return {names[i]: float(probs[i]) for i in range(len(names))}
+                    
+examples = ['16_image_06670.jpg','33_image_06460.jpg','80_image_02020.jpg', 'Flowers.png','inference_example.png']
+title = "Image Classifier - Species of Flower predicted by different Models"
+description = "Image classifiers to recognize different species of flowers trained on 102 Category Flower Dataset"
+article = article="<p style='text-align: center'><a href='https://www.robots.ox.ac.uk/~vgg/data/flowers/102/index.html' target='_blank'>Source 102 Flower Dataset</a></p>"
+interpretation = 'default'
+enable_queue = True
+iface = gr.Interface(fn=process_input, inputs=gr.inputs.Image(type='filepath'), outputs=[gr.outputs.Label(num_top_classes=3), gr.outputs.Label(num_top_classes=3)], examples = examples, title=title, description=description,article=article,interpretation=interpretation, enable_queue=enable_queue
+                    )
+iface.launch()
+
+#(num_top_classes=3)q