closes #41
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bc801648a3
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@ -79,7 +79,7 @@ func generateCvs(c *Context, run_path string, model_id string) (count int, err e
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return
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}
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data, err := c.Db.Query("select mdp.id, mc.class_order, mdp.file_path from model_data_point as mdp inner join model_classes as mc on mc.id = mdp.class_id where mc.model_id = $1;", model_id)
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data, err := c.Db.Query("select mdp.id, mc.class_order, mdp.file_path from model_data_point as mdp inner join model_classes as mc on mc.id = mdp.class_id where mc.model_id = $1 and mdp.model_mode=$2;", model_id, model_classes.DATA_POINT_MODE_TRAINING)
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if err != nil {
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return
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}
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@ -74,14 +74,23 @@ def prepare_dataset(ds: tf.data.Dataset) -> tf.data.Dataset:
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ds = configure_for_performance(ds)
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return ds
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def filterDataset(path):
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path = tf.strings.regex_replace(path, DATA_DIR_PREPARE, "")
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path = tf.strings.regex_replace(path, ".jpg", "")
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path = tf.strings.regex_replace(path, ".jpeg", "")
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return tf.reshape(table.lookup(tf.strings.as_string([path])), []) != -1
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seed = random.randint(0, 100000000)
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batch_size = 100
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# Read all the files from the direcotry
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list_ds = tf.data.Dataset.list_files(str(f'{DATA_DIR}/*'), shuffle=False)
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list_ds = list_ds.filter(filterDataset)
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image_count = len(list_ds)
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image_count = len(list(list_ds.as_numpy_iterator()))
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list_ds = list_ds.shuffle(image_count, seed=seed)
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