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app.py

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+import gradio as gr
+import numpy as np # linear algebra
+import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
+import os
+import torch
+import torchvision
+import torch.nn as nn # All neural network modules, nn.Linear, nn.Conv2d, BatchNorm, Loss functions
+import torchvision.datasets as datasets # Has standard datasets we can import in a nice way
+import torchvision.transforms as transforms # Transformations we can perform on our dataset
+import torch.nn.functional as F # All functions that don't have any parameters
+from torch.utils.data import DataLoader, Dataset # Gives easier dataset managment and creates mini batches
+from torchvision.datasets import ImageFolder
+import torch.optim as optim # For all Optimization algorithms, SGD, Adam, etc.
+from PIL import Image
+
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu") # use gpu or cpu
+
+from tqdm import tqdm
+from torchvision import models
+# load pretrain model and modify...
+model = models.resnet50(pretrained=True)
+
+# If you want to do finetuning then set requires_grad = False
+# Remove these two lines if you want to train entire model,
+# and only want to load the pretrain weights.
+
+for param in model.parameters():
+    param.requires_grad = False
+
+num_ftrs = model.fc.in_features
+model.fc = nn.Linear(num_ftrs, 2)
+
+model.to(device)
+
+# Loss and optimizer
+criterion = nn.CrossEntropyLoss()
+optimizer = optim.Adam(model.parameters(), lr=0.01)
+
+checkpoint = torch.load("D:\cats_dogs\cats_dogs\checpoint_epoch_4.pt",
+                       map_location=torch.device('cpu'))
+model.load_state_dict(checkpoint["model_state_dict"])
+optimizer.load_state_dict(checkpoint["optimizer_state_dict"])
+
+
+def image_classifier(inp):
+    model.eval()
+
+    data_transforms = transforms.Compose([
+        transforms.ToTensor(),
+        transforms.Resize((224, 224)),
+        transforms.Normalize([0.5] * 3, [0.5] * 3), ])
+    img = data_transforms(inp).unsqueeze(dim=0)
+
+    img = img.to(device)
+    pred = model(img)
+    _, preds = torch.max(pred, 1)
+    print(f"class : {preds}")
+    cur_name = ""
+    if preds[0] == 1:
+        print(f"predicted ----> Dog")
+        cur_name = "DOG"
+    else:
+        print(f"predicted ----> Cat")
+        cur_name = "CAT"
+
+    return cur_name
+
+
+demo = gr.Interface(fn=image_classifier, inputs="image", outputs="text")
+demo.launch()

checpoint_epoch_4.pt

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+version https://git-lfs.github.com/spec/v1
+oid sha256:f4041e5a95287674e8b88731d4521bdb52dd593f54931f84f7b75fcf7f59a6c6
+size 94407571