added simple eval only script.

eval_only.py

@@ -0,0 +1,72 @@
+"""Eval model from huggingface on gold."""
+import os
+import logging
+
+import torch
+from transformers import AutoTokenizer, AutoModelForSequenceClassification
+
+from mbdataset import MLDatasetWithFloats
+from processors import MultiLabelTSVProcessor
+from mutils import write_metrics, perform_inference
+
+model_dir = "lrei/roberta-base-emolit"
+print(f"Model = {model_dir}")
+output_dir = "/data"
+os.makedirs(output_dir, exist_ok=True)
+
+
+gold_file = "./data/emolit/gold.tsv"
+SEQLEN = 40
+DEVICE = 0
+
+logger = logging.getLogger(__name__)
+logging.basicConfig(
+    format="%(asctime)s - %(levelname)s - %(name)s -   %(message)s",
+    datefmt="%m/%d/%Y %H:%M:%S",
+    level=logging.INFO,
+)
+os.makedirs(output_dir, exist_ok=True)
+
+
+# Load model and tokenizer
+model = AutoModelForSequenceClassification.from_pretrained(model_dir)
+model = model.to(dtype=torch.float16, device=DEVICE)
+model.eval()
+param_size = 0
+for param in model.parameters():
+    param_size += param.nelement() * param.element_size()
+buffer_size = 0
+for buffer in model.buffers():
+    buffer_size += buffer.nelement() * buffer.element_size()
+
+size_all_mb = (param_size + buffer_size) / 1024**2
+print('model size: {:.3f}MB'.format(size_all_mb))
+print('model size: {}'.format(param_size))
+tokenizer = AutoTokenizer.from_pretrained(model_dir)
+
+
+# Load data
+id2label = model.config.id2label
+print(id2label)
+label2id = model.config.label2id
+if label2id is None:
+    label2id = {v: k for k, v in id2label.items()}  # type: ignore
+
+
+processor_tst = MultiLabelTSVProcessor(data_file=gold_file)
+le = MLDatasetWithFloats.create_label_encoder_from_id2label(
+    id2label=id2label
+)
+target_names = le.classes_.tolist()
+tst_dataset = MLDatasetWithFloats(processor_tst, tokenizer, SEQLEN, le=le)
+preds = perform_inference(model, tst_dataset)
+labels = tst_dataset.get_label_ids()
+results_file = os.path.join(output_dir, "results.txt")
+write_metrics(
+    labels,
+    preds,
+    target_names=target_names,
+    output_file=results_file,
+    expand_neutral=None,
+)
+