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minigpt4/common/eval_utils.py

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+import argparse
+import numpy as np
+from nltk.translate.bleu_score import sentence_bleu
+
+from minigpt4.common.registry import registry
+from minigpt4.common.config import Config
+
+# imports modules for registration
+from minigpt4.datasets.builders import *
+from minigpt4.models import *
+from minigpt4.processors import *
+from minigpt4.runners import *
+from minigpt4.tasks import *
+
+
+
+def eval_parser():
+    parser = argparse.ArgumentParser(description="Demo")
+    parser.add_argument("--cfg-path", required=True, help="path to configuration file.")
+    parser.add_argument("--name", type=str, default='A2', help="evaluation name")
+    parser.add_argument("--ckpt", type=str, help="path to configuration file.")
+    parser.add_argument("--eval_opt", type=str, default='all', help="path to configuration file.")
+    parser.add_argument("--max_new_tokens", type=int, default=10, help="max number of generated tokens")
+    parser.add_argument("--batch_size", type=int, default=32)
+    parser.add_argument("--lora_r", type=int, default=64, help="lora rank of the model")
+    parser.add_argument("--lora_alpha", type=int, default=16, help="lora alpha")
+    parser.add_argument(
+        "--options",
+        nargs="+",
+        help="override some settings in the used config, the key-value pair "
+             "in xxx=yyy format will be merged into config file (deprecate), "
+             "change to --cfg-options instead.",
+    )
+    return parser
+
+
+def prepare_texts(texts, conv_temp):
+    convs = [conv_temp.copy() for _ in range(len(texts))]
+    [conv.append_message(
+        conv.roles[0], '<Img><ImageHere></Img> {}'.format(text)) for conv, text in zip(convs, texts)]
+    [conv.append_message(conv.roles[1], None) for conv in convs]
+    texts = [conv.get_prompt() for conv in convs]
+    return texts
+
+
+def init_model(args):
+    print('Initialization Model')
+    cfg = Config(args)
+    cfg.model_cfg.ckpt = args.ckpt
+    cfg.model_cfg.lora_r = args.lora_r
+    cfg.model_cfg.lora_alpha = args.lora_alpha
+
+    model_config = cfg.model_cfg
+    model_cls = registry.get_model_class(model_config.arch)
+    model = model_cls.from_config(model_config).to('cuda:0')
+
+#     import pudb; pudb.set_trace()
+    key = list(cfg.datasets_cfg.keys())[0]
+    vis_processor_cfg = cfg.datasets_cfg.get(key).vis_processor.train
+    vis_processor = registry.get_processor_class(vis_processor_cfg.name).from_config(vis_processor_cfg)
+    print('Initialization Finished')
+    return model, vis_processor
+
+def computeIoU(bbox1, bbox2):
+    x1, y1, x2, y2 = bbox1
+    x3, y3, x4, y4 = bbox2
+    intersection_x1 = max(x1, x3)
+    intersection_y1 = max(y1, y3)
+    intersection_x2 = min(x2, x4)
+    intersection_y2 = min(y2, y4)
+    intersection_area = max(0, intersection_x2 - intersection_x1 + 1) * max(0, intersection_y2 - intersection_y1 + 1)
+    bbox1_area = (x2 - x1 + 1) * (y2 - y1 + 1)
+    bbox2_area = (x4 - x3 + 1) * (y4 - y3 + 1)
+    union_area = bbox1_area + bbox2_area - intersection_area
+    iou = intersection_area / union_area
+    return iou