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58 lines
1.8 KiB
Python
58 lines
1.8 KiB
Python
import logging
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import os
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import shutil
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import pandas as pd
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import torch
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import yaml
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from torchvision.utils import save_image
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from ludwig.api import LudwigModel
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from ludwig.datasets import camseq
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# clean out prior results
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shutil.rmtree("./results", ignore_errors=True)
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# set up Python dictionary to hold model training parameters
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with open("./config_camseq.yaml") as f:
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config = yaml.safe_load(f.read())
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# Define Ludwig model object that drive model training
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model = LudwigModel(config, logging_level=logging.INFO)
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# load Camseq dataset
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df = camseq.load(split=False)
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pred_set = df[0:1] # prediction hold-out 1 image
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data_set = df[1:] # train,test,validate on remaining images
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# initiate model training
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train_stats, _, output_directory = model.train( # training statistics # location for training results saved to disk
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dataset=data_set,
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experiment_name="simple_image_experiment",
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model_name="single_model",
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skip_save_processed_input=True,
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)
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# print("{}".format(model.model))
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# predict
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pred_set.reset_index(inplace=True)
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pred_out_df, results = model.predict(pred_set)
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if not isinstance(pred_out_df, pd.DataFrame):
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pred_out_df = pred_out_df.compute()
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pred_out_df["image_path"] = pred_set["image_path"]
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pred_out_df["mask_path"] = pred_set["mask_path"]
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for index, row in pred_out_df.iterrows():
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pred_mask = torch.from_numpy(row["mask_path_predictions"])
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pred_mask_path = os.path.dirname(os.path.realpath(__file__)) + "/predicted_" + os.path.basename(row["mask_path"])
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print(f"\nSaving predicted mask to {pred_mask_path}")
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if torch.any(pred_mask.gt(1)):
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pred_mask = pred_mask.float() / 255
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save_image(pred_mask, pred_mask_path)
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print("Input image_path: {}".format(row["image_path"]))
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print("Label mask_path: {}".format(row["mask_path"]))
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print(f"Predicted mask_path: {pred_mask_path}")
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