train.py

import torch
from datasets import load_dataset
import evaluate
from transformers import EfficientNetImageProcessor, EfficientNetForImageClassification, TrainingArguments, Trainer
import numpy as np

print("Cuda availability:", torch.cuda.is_available())
cuda = torch.device('cuda')     # Default HIP device
print("cuda: ", torch.cuda.get_device_name(device=cuda))

dataset = load_dataset("chriamue/bird-species-dataset")

model_name = "google/efficientnet-b2"
finetuned_model_name = "chriamue/bird-species-classifier"

#####
labels = dataset["train"].features["label"].names
label2id, id2label = dict(), dict()
for i, label in enumerate(labels):
    label2id[label] = str(i)
    id2label[str(i)] = label

preprocessor = EfficientNetImageProcessor.from_pretrained(model_name)
model = EfficientNetForImageClassification.from_pretrained(model_name, num_labels=len(
    labels), id2label=id2label, label2id=label2id, ignore_mismatched_sizes=True)


training_args = TrainingArguments(
    finetuned_model_name, remove_unused_columns=False,
    evaluation_strategy="epoch",
    save_strategy="epoch",
    learning_rate=5e-5,
    per_device_train_batch_size=32,
    per_device_eval_batch_size=16,
    num_train_epochs=1,
    weight_decay=0.01,
    load_best_model_at_end=True,
    metric_for_best_model="accuracy"
)

metric = evaluate.load("accuracy")


def compute_metrics(eval_pred):
    predictions, labels = eval_pred
    predictions = np.argmax(predictions, axis=1)
    return metric.compute(predictions=predictions, references=labels)


def transforms(examples):
    pixel_values = [preprocessor(image, return_tensors="pt").pixel_values.squeeze(
        0) for image in examples["image"]]
    examples["pixel_values"] = pixel_values
    return examples


image = dataset["train"][0]["image"]

dataset["train"] = dataset["train"].shuffle(seed=42).select(range(1500))
# dataset["validation"] = dataset["validation"].select(range(100))
# dataset["test"] = dataset["test"].select(range(100))

dataset = dataset.map(transforms, remove_columns=["image"], batched=True)

trainer = Trainer(
    model=model,
    args=training_args,
    train_dataset=dataset["train"],
    eval_dataset=dataset["validation"],
    compute_metrics=compute_metrics,
)

train_results = trainer.train(resume_from_checkpoint=True)

trainer.evaluate()

# trainer.save_model("test_trainer")

# trainer.save_model()
trainer.log_metrics("train", train_results.metrics)
# trainer.save_metrics("train", train_results.metrics)
# trainer.save_state()

dummy_input = torch.randn(1, 3, 224, 224)
model = model.to('cpu')
output_onnx_path = 'model.onnx'
torch.onnx.export(model, dummy_input, output_onnx_path, export_params=True, opset_version=13, do_constant_folding=True, input_names=['input'], output_names=['output'], dynamic_axes={'input': {0: 'batch_size'}, 'output': {0: 'batch_size'}})


#device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
#model = model.to(device)

inputs = preprocessor(image, return_tensors="pt")
#inputs = {k: v.to(device) for k, v in inputs.items()}

with torch.no_grad():
    logits = model(**inputs).logits
    predicted_label = logits.argmax(-1).item()
    print(labels[predicted_label])