Inference after Finetuning
Closed this issue · 1 comments
mcdulltii commented
May I know the way to use the .pt files after finetuning is performed on a dataset? I would like to load the .pt file and run inference on a single image, then print out the classification result from it, i.e. the classname.
Below is the command and output of my finetuning.
python -m src.wise_ft \
--train-dataset=RN50 \
--epochs=20 \
--lr=0.00003 \
--batch-size=32 \
--cache-dir=cache \
--model=RN50 \
--eval-datasets=RN50 \
--classnames="['true', 'false']" \
--template=custom_dataset_template \
--results-db=results.jsonl \
--save=models/wiseft/custom \
--data-location=/content/dataset \
--alpha 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0
Getting zeroshot weights.
100% 48/48 [00:01<00:00, 34.05it/s]
Saving image classifier to models/wiseft/custom/zeroshot.pt
Loading image classifier from models/wiseft/custom/zeroshot.pt
Fine-tuning end-to-end
Using devices [0]
Train Epoch: 0 [0% 0/21] Loss: 3.895792 Data (t) 0.025 Batch (t) 2.184
Saving model to models/wiseft/custom/finetuned/checkpoint_1.pt
Saving image classifier to models/wiseft/custom/finetuned/checkpoint_1.pt
Evaluating on RN50
RN50 Top-1 accuracy: 0.1944
Results saved to results.jsonl.
Train Epoch: 1 [0% 0/21] Loss: 3.722734 Data (t) 0.097 Batch (t) 0.668
Saving model to models/wiseft/custom/finetuned/checkpoint_2.pt
Saving image classifier to models/wiseft/custom/finetuned/checkpoint_2.pt
Evaluating on RN50
RN50 Top-1 accuracy: 0.4444
Results saved to results.jsonl.
Train Epoch: 2 [0% 0/21] Loss: 3.029700 Data (t) 0.103 Batch (t) 1.164
Saving model to models/wiseft/custom/finetuned/checkpoint_3.pt
Saving image classifier to models/wiseft/custom/finetuned/checkpoint_3.pt
Evaluating on RN50
RN50 Top-1 accuracy: 0.5417
Results saved to results.jsonl.
Train Epoch: 3 [0% 0/21] Loss: 2.278040 Data (t) 0.104 Batch (t) 1.193
Saving model to models/wiseft/custom/finetuned/checkpoint_4.pt
Saving image classifier to models/wiseft/custom/finetuned/checkpoint_4.pt
Evaluating on RN50
RN50 Top-1 accuracy: 0.5694
Results saved to results.jsonl.
Train Epoch: 4 [0% 0/21] Loss: 1.359167 Data (t) 0.205 Batch (t) 0.938
Saving model to models/wiseft/custom/finetuned/checkpoint_5.pt
Saving image classifier to models/wiseft/custom/finetuned/checkpoint_5.pt
Evaluating on RN50
RN50 Top-1 accuracy: 0.6250
Results saved to results.jsonl.
Train Epoch: 5 [0% 0/21] Loss: 1.342139 Data (t) 0.096 Batch (t) 1.317
Saving model to models/wiseft/custom/finetuned/checkpoint_6.pt
Saving image classifier to models/wiseft/custom/finetuned/checkpoint_6.pt
Evaluating on RN50
RN50 Top-1 accuracy: 0.6389
Results saved to results.jsonl.
Train Epoch: 6 [0% 0/21] Loss: 1.333306 Data (t) 0.091 Batch (t) 1.026
Saving model to models/wiseft/custom/finetuned/checkpoint_7.pt
Saving image classifier to models/wiseft/custom/finetuned/checkpoint_7.pt
Evaluating on RN50
RN50 Top-1 accuracy: 0.7083
Results saved to results.jsonl.
Train Epoch: 7 [0% 0/21] Loss: 0.917647 Data (t) 0.084 Batch (t) 1.141
Saving model to models/wiseft/custom/finetuned/checkpoint_8.pt
Saving image classifier to models/wiseft/custom/finetuned/checkpoint_8.pt
Evaluating on RN50
RN50 Top-1 accuracy: 0.8333
Results saved to results.jsonl.
Train Epoch: 8 [0% 0/21] Loss: 0.812102 Data (t) 0.099 Batch (t) 0.603
Saving model to models/wiseft/custom/finetuned/checkpoint_9.pt
Saving image classifier to models/wiseft/custom/finetuned/checkpoint_9.pt
Evaluating on RN50
RN50 Top-1 accuracy: 0.7917
Results saved to results.jsonl.
Train Epoch: 9 [0% 0/21] Loss: 1.104869 Data (t) 0.148 Batch (t) 0.852
Saving model to models/wiseft/custom/finetuned/checkpoint_10.pt
Saving image classifier to models/wiseft/custom/finetuned/checkpoint_10.pt
Evaluating on RN50
RN50 Top-1 accuracy: 0.8472
Results saved to results.jsonl.
Train Epoch: 10 [0% 0/21] Loss: 0.608275 Data (t) 0.205 Batch (t) 0.761
Saving model to models/wiseft/custom/finetuned/checkpoint_11.pt
Saving image classifier to models/wiseft/custom/finetuned/checkpoint_11.pt
Evaluating on RN50
RN50 Top-1 accuracy: 0.8611
Results saved to results.jsonl.
Train Epoch: 11 [0% 0/21] Loss: 0.696144 Data (t) 0.096 Batch (t) 0.904
Saving model to models/wiseft/custom/finetuned/checkpoint_12.pt
Saving image classifier to models/wiseft/custom/finetuned/checkpoint_12.pt
Evaluating on RN50
RN50 Top-1 accuracy: 0.8194
Results saved to results.jsonl.
Train Epoch: 12 [0% 0/21] Loss: 0.845205 Data (t) 0.165 Batch (t) 1.253
Saving model to models/wiseft/custom/finetuned/checkpoint_13.pt
Saving image classifier to models/wiseft/custom/finetuned/checkpoint_13.pt
Evaluating on RN50
RN50 Top-1 accuracy: 0.8611
Results saved to results.jsonl.
Train Epoch: 13 [0% 0/21] Loss: 0.884099 Data (t) 0.147 Batch (t) 0.730
Saving model to models/wiseft/custom/finetuned/checkpoint_14.pt
Saving image classifier to models/wiseft/custom/finetuned/checkpoint_14.pt
Evaluating on RN50
RN50 Top-1 accuracy: 0.8611
Results saved to results.jsonl.
Train Epoch: 14 [0% 0/21] Loss: 1.082862 Data (t) 0.124 Batch (t) 0.846
Saving model to models/wiseft/custom/finetuned/checkpoint_15.pt
Saving image classifier to models/wiseft/custom/finetuned/checkpoint_15.pt
Evaluating on RN50
RN50 Top-1 accuracy: 0.8333
Results saved to results.jsonl.
Train Epoch: 15 [0% 0/21] Loss: 0.864339 Data (t) 0.082 Batch (t) 1.392
Saving model to models/wiseft/custom/finetuned/checkpoint_16.pt
Saving image classifier to models/wiseft/custom/finetuned/checkpoint_16.pt
Evaluating on RN50
RN50 Top-1 accuracy: 0.8333
Results saved to results.jsonl.
Train Epoch: 16 [0% 0/21] Loss: 0.649688 Data (t) 0.128 Batch (t) 0.876
Saving model to models/wiseft/custom/finetuned/checkpoint_17.pt
Saving image classifier to models/wiseft/custom/finetuned/checkpoint_17.pt
Evaluating on RN50
RN50 Top-1 accuracy: 0.7778
Results saved to results.jsonl.
Train Epoch: 17 [0% 0/21] Loss: 0.560998 Data (t) 0.205 Batch (t) 1.278
Saving model to models/wiseft/custom/finetuned/checkpoint_18.pt
Saving image classifier to models/wiseft/custom/finetuned/checkpoint_18.pt
Evaluating on RN50
RN50 Top-1 accuracy: 0.7500
Results saved to results.jsonl.
Train Epoch: 18 [0% 0/21] Loss: 0.761688 Data (t) 0.155 Batch (t) 1.089
Saving model to models/wiseft/custom/finetuned/checkpoint_19.pt
Saving image classifier to models/wiseft/custom/finetuned/checkpoint_19.pt
Evaluating on RN50
RN50 Top-1 accuracy: 0.8750
Results saved to results.jsonl.
Train Epoch: 19 [0% 0/21] Loss: 0.621387 Data (t) 0.065 Batch (t) 0.995
Saving model to models/wiseft/custom/finetuned/checkpoint_20.pt
Saving image classifier to models/wiseft/custom/finetuned/checkpoint_20.pt
Evaluating on RN50
RN50 Top-1 accuracy: 0.8750
Results saved to results.jsonl.
Loading image classifier from models/wiseft/custom/zeroshot.pt
Loading image classifier from models/wiseft/custom/finetuned/checkpoint_20.pt
Saving image classifier to models/wiseft/custom/finetuned/wise_ft_alpha=0.000.pt
Evaluating on RN50
RN50 Top-1 accuracy: 0.0000
Results saved to results.jsonl.
Saving image classifier to models/wiseft/custom/finetuned/wise_ft_alpha=0.100.pt
Evaluating on RN50
RN50 Top-1 accuracy: 0.0000
Results saved to results.jsonl.
Saving image classifier to models/wiseft/custom/finetuned/wise_ft_alpha=0.200.pt
Evaluating on RN50
RN50 Top-1 accuracy: 0.0278
Results saved to results.jsonl.
Saving image classifier to models/wiseft/custom/finetuned/wise_ft_alpha=0.300.pt
Evaluating on RN50
RN50 Top-1 accuracy: 0.2500
Results saved to results.jsonl.
Saving image classifier to models/wiseft/custom/finetuned/wise_ft_alpha=0.400.pt
Evaluating on RN50
RN50 Top-1 accuracy: 0.4306
Results saved to results.jsonl.
Saving image classifier to models/wiseft/custom/finetuned/wise_ft_alpha=0.500.pt
Evaluating on RN50
RN50 Top-1 accuracy: 0.5139
Results saved to results.jsonl.
Saving image classifier to models/wiseft/custom/finetuned/wise_ft_alpha=0.600.pt
Evaluating on RN50
RN50 Top-1 accuracy: 0.6250
Results saved to results.jsonl.
Saving image classifier to models/wiseft/custom/finetuned/wise_ft_alpha=0.700.pt
Evaluating on RN50
RN50 Top-1 accuracy: 0.6667
Results saved to results.jsonl.
Saving image classifier to models/wiseft/custom/finetuned/wise_ft_alpha=0.800.pt
Evaluating on RN50
RN50 Top-1 accuracy: 0.7500
Results saved to results.jsonl.
Saving image classifier to models/wiseft/custom/finetuned/wise_ft_alpha=0.900.pt
Evaluating on RN50
RN50 Top-1 accuracy: 0.8472
Results saved to results.jsonl.
Saving image classifier to models/wiseft/custom/finetuned/wise_ft_alpha=1.000.pt
Evaluating on RN50
RN50 Top-1 accuracy: 0.8750
Results saved to results.jsonl.
mcdulltii commented
For anyone with a custom dataset that wishes to use the model for inference, refer to the eval_single_dataset() function in src/models/eval.py. The true labels, inference logits can be found in all_labels and all_preds (Line 31) respectively, whereas the predicted label for every image in the dataset can be found in pred (Line 50). Extract them after running the function on the custom dataset for any post visualizations/processings.