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best_model_mb_ft.pth

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+version https://git-lfs.github.com/spec/v1
+oid sha256:b4aee2952b1e7bda1673648f7917b0743230bd72a61e5ce747fb9c1c88f0300d
+size 17752814

grocery_predict.py

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+from __future__ import print_function, division
+
+import torch
+import torch.nn as nn
+import torch.optim as optim
+import torchvision
+from torchvision import datasets, models, transforms
+import os
+from torch.utils.data import DataLoader
+import numpy as np
+from tqdm import tqdm
+from timeit import default_timer as timer
+from datasets import load_dataset
+from datasets import load_metric
+import evaluate
+from train import *
+from transformers import AutoImageProcessor, MobileViTV2ForImageClassification, MobileViTV2Config
+
+
+def collate_fn(batch):
+    reviews, labels = zip(*batch)
+    labels = torch.Tensor(labels)
+    return reviews, labels
+
+
+def compute_metrics(eval_pred):
+    metric = load_metric("accuracy")
+    predictions = np.argmax(eval_pred.predictions, axis=1)
+    return metric.compute(predictions=predictions, references=eval_pred.label_ids)
+
+
+
+
+if __name__ == '__main__':
+    # Define path
+    # device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+    device = torch.device("cpu")
+
+    testdir = f"data/grocery_data_small/test"
+    # Change to fit hardware
+    num_workers = 0
+    batch_size = 8
+    num_epochs = 5
+    learning_rate = 0.001
+    max_epochs_stop = 3
+    print_every = 1
+    num_classes = 8
+    # define image transformations
+    # define transforms
+
+    # Datasets from folders
+
+    data = datasets.ImageFolder(root=testdir, transform=transforms.ToTensor())
+
+
+    # Dataloader iterators, make sure to shuffle
+
+    dataloader = DataLoader(data, batch_size=batch_size, shuffle=True,
+                            num_workers=num_workers, collate_fn=collate_fn)
+
+
+    # Define the network with pretrained models
+
+    model_checkpoint = "apple/mobilevitv2-1.0-imagenet1k-256"
+
+    processor = AutoImageProcessor.from_pretrained(model_checkpoint)
+    model = MobileViTV2ForImageClassification.from_pretrained(model_checkpoint, num_labels=num_classes,
+                                                              ignore_mismatched_sizes=True)
+
+    #model = MobileViTV2ForImageClassification(MobileViTV2Config())
+    #model.classifier = nn.Linear(model.classifier.in_features, num_classes)
+    print(model.classifier)
+
+    model.load_state_dict(torch.load('best_model_mb_ft.pth'))
+    model.eval()
+    correct = 0
+    total = 0
+
+    with torch.no_grad():
+        for inputs, labels in tqdm(dataloader, desc="predicting"):
+            inputs = processor(images=inputs, return_tensors="pt")
+
+            outputs = model(**inputs)
+            logits = outputs.logits
+
+            predicted = logits.argmax(-1)
+
+            total += labels.size(0)
+            correct += (predicted == labels).sum().item()
+
+    accuracy = correct / total * 100
+
+    print(f"Predict Accuracy: {accuracy:.2f}%")
+