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import os |
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import tempfile |
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import json |
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from uniperceiver.config import configurable |
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from .build import EVALUATION_REGISTRY |
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from uniperceiver.utils import comm |
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import numpy as np |
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def simple_accuracy(preds, labels): |
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return (preds == labels).mean() |
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@EVALUATION_REGISTRY.register() |
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class MiTEvaler(object): |
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def __init__(self, cfg, annfile, output_dir): |
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super(MiTEvaler, self).__init__() |
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self.video_dict = dict() |
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self.cls2idx = dict() |
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with open(os.path.join(os.path.dirname(annfile), "category_mapping.txt"), 'r') as f: |
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for line in f.readlines(): |
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class_name, idx = line.strip().split('\t') |
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class_name = class_name.replace(" ", "_") |
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self.cls2idx[class_name] = int(idx) |
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with open(annfile) as f: |
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data_file = json.load(f) |
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for name, info in data_file['database'].items(): |
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if info['subset'] == "validation": |
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self.video_dict[name] = self.cls2idx[info['annotations']['label']] |
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if not os.path.exists(comm.TEMP_DIR): |
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os.mkdir(comm.TEMP_DIR) |
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if output_dir is not None: |
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self.output_dir = os.path.join(output_dir, 'results') |
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if not os.path.exists(self.output_dir) and comm.is_main_process(): |
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os.mkdir(self.output_dir) |
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else: |
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self.output_dir = None |
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def eval(self, results, epoch): |
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preds = [] |
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labels = [] |
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for result in results: |
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labels.append(self.video_dict[result["video_name"]]) |
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preds.append(result["label"].item()) |
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preds = np.array(preds) |
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labels = np.array(labels) |
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acc = simple_accuracy(preds, labels) |
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return {"accuracy": acc} |
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