1a0400d736
ToDo: Visualization for variational spaces Trajectory Coloring Post Processing Metric Slurm Skript
82 lines
2.8 KiB
Python
82 lines
2.8 KiB
Python
# TODO: THIS
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import seaborn as sb
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import torch
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from torch.utils.data import DataLoader
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from pytorch_lightning import data_loader
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from dataset import DataContainer
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from collections import defaultdict
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from tqdm import tqdm
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import os
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from sklearn.manifold import TSNE
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from sklearn.decomposition import PCA
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import seaborn as sns; sns.set()
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import matplotlib.pyplot as plt
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from run_models import *
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def search_for_weights(folder):
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while not os.path.exists(folder):
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if len(os.path.split(folder)) >= 50:
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raise FileNotFoundError(f'The folder "{folder}" could not be found')
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folder = os.path.join(os.pardir, folder)
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for element in os.scandir(folder):
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if os.path.exists(element):
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if element.is_dir():
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search_for_weights(element.path)
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elif element.is_file() and element.name.endswith('.ckpt'):
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load_and_predict(element)
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else:
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continue
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def load_and_predict(path_like_element):
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# Define Loop to search for models and folder with visualizations
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model = globals()[path_like_element.path.split(os.sep)[-3]]
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pretrained_model = model.load_from_metrics(
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weights_path=path_like_element.path,
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tags_csv=os.path.join(os.path.dirname(path_like_element), 'default', 'version_0', 'meta_tags.csv'),
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on_gpu=True if torch.cuda.is_available() else False,
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map_location=None
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)
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# Init model and freeze its weights ( for faster inference)
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pretrained_model.eval()
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pretrained_model.freeze()
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# Load the data for prediction
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dataset = DataContainer(os.path.join(os.pardir, 'data'), 5, 5)
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# Do the inference
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prediction_dict = defaultdict(list)
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for i in tqdm(range(len(dataset)), total=len(dataset)):
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p_X = pretrained_model(dataset[i].unsqueeze(0))
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for idx in range(len(p_X) - 1):
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prediction_dict[idx].append(p_X[idx])
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predictions = [torch.cat(prediction).detach().numpy() for prediction in prediction_dict.values()]
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for prediction in predictions:
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viz_latent(prediction)
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def viz_latent(prediction):
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if prediction.shape[-1] <= 1:
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raise ValueError('How did this happen?')
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elif prediction.shape[-1] == 2:
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ax = sns.scatterplot(x=prediction[:, 0], y=prediction[:, 1])
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plt.show()
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return ax
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else:
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fig, axs = plt.subplots(ncols=2)
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predictions_pca = PCA(n_components=2)
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predictions_tsne = TSNE(n_components=2)
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pca_plot = sns.scatterplot(x=predictions_pca[:, 0], y=predictions_pca[:, 1], ax=axs[0])
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tsne_plot = sns.scatterplot(x=predictions_tsne[:, 0], y=predictions_tsne[:, 1], ax=axs[1])
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plt.show()
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return fig, axs, pca_plot, tsne_plot
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if __name__ == '__main__':
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path = 'output'
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search_for_weights(path) |