Visualization approach n
This commit is contained in:
Si11ium
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viz/output.png
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viz/output.png
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viz/print_movement_in_map.py
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viz/print_movement_in_map.py
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@ -0,0 +1,50 @@
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from argparse import ArgumentParser
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import os
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from dataset import DataContainer
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from viz.utils import MotionAnalyser, Printer, MapContainer, search_for_weights
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import torch
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from run_models import SAAE_Model, AAE_Model, VAE_Model, AE_Model
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arguments = ArgumentParser()
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arguments.add_argument('--data', default='output')
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device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
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def load_and_viz(path_like_element):
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# Define Loop to search for models and folder with visualizations
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splitpath = path_like_element.split(os.sep)
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base_dir = os.path.join(*splitpath[:4])
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model = globals()[splitpath[2]]
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print(f'... loading model named: "{model.name}" from timestamp: {splitpath[3]}')
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pretrained_model = model.load_from_metrics(
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weights_path=path_like_element,
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tags_csv=os.path.join(base_dir, '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 = pretrained_model.to(device)
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pretrained_model.eval()
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pretrained_model.freeze()
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dataIndex = 0
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datasets = DataContainer(os.path.join(os.pardir, 'data', 'validation'), 9, 6).to(device)
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dataset = datasets.datasets[dataIndex]
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# ToDO: use dataloader for iteration instead! - dataloader = DataLoader(dataset, )
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maps = MapContainer(os.path.join(os.pardir, 'data', 'validation'))
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base_map = maps.datasets[dataIndex]
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p = Printer(pretrained_model)
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p.print_trajec_on_basemap(dataset, base_map, save=os.path.join(base_dir, f'{base_map.name}_movement.png'),
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color_by_movement=True)
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return True
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if __name__ == '__main__':
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args = arguments.parse_args()
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search_for_weights(load_and_viz, args.data, file_type='movement')
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341
viz/utils.py
341
viz/utils.py
@ -1,13 +1,30 @@
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import os
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from typing import Union
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from functools import reduce
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from statistics import stdev
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from sklearn.cluster import Birch, KMeans, DBSCAN
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from sklearn.manifold import TSNE
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from sklearn.decomposition import PCA
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from dataset import *
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from networks.modules import AbstractNeuralNetwork
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from matplotlib import pyplot as plt
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from matplotlib.patches import Polygon
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from matplotlib.collections import LineCollection, PatchCollection
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import matplotlib.colors as mcolors
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import matplotlib.cm as cmaps
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from math import pi
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def search_for_weights(func, folder):
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def search_for_weights(func, folder, file_type='latent_space'):
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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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if any([x.name.endswith('.png') for x in os.scandir(folder)]):
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if any([file_type in x.name for x in os.scandir(folder)]):
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return
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if any(['.ckpt' in element.name and element.is_dir() for element in os.scandir(folder)]):
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@ -19,12 +36,324 @@ def search_for_weights(func, 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(func, element.path)
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search_for_weights(func, element.path, file_type=file_type)
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elif element.is_file() and element.name.endswith('.ckpt'):
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func(element)
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func(element.path)
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else:
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continue
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class Printer(object):
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def __init__(self, model: AbstractNeuralNetwork, ax=None):
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self.norm = mcolors.Normalize(vmin=0, vmax=1)
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self.colormap = cmaps.gist_rainbow
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self.network = model
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self.fig = plt.figure(dpi=300)
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self.ax = ax if ax else plt.subplot(1, 1, 1)
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pass
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def colorize(self, x, min_val: Union[float, None] = None, max_val: Union[float, None] = None,
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colormap=cmaps.rainbow, **kwargs):
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norm = mcolors.Normalize(vmin=min_val, vmax=max_val)
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colored = colormap(norm(x))
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return colored
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@staticmethod
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def project_to_2d(data: np.ndarray, method: str = 'tsne') -> np.ndarray:
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projector = TSNE() if method.lower() == 'tsne' else PCA()
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print('Starting TSNE Transformation')
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projected_data = projector.fit_transform(data)
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assert projected_data.shape[-1] == 2
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print('TSNE Projection Successfull')
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return projected_data
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@staticmethod
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def cluster_data(data: np.ndarray, cluster_center_file: str = None) -> np.ndarray:
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print('Start Clustering with Birch')
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if cluster_center_file:
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with open(cluster_center_file, 'r') as f:
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cluster_center_string = f.readlines()[0]
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centers = ast.literal_eval(cluster_center_string)
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clusterer = Birch(n_clusters=len(centers))
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clusterer.init = np.asarray(centers)
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else:
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# clusterer = Birch(n_clusters=None)
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clusterer = Birch()
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labels = clusterer.fit_predict(data)
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print('Birch Clustering Sucessfull')
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return labels
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def print_possible_latent_spaces(self, data: Trajectories, n: Union[int, str] = 1000, **kwargs):
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predictions, _ = self._gather_predictions(data, n)
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if len(predictions) >= 2:
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predictions += (torch.cat(predictions, dim=-1), )
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labels = self.cluster_data(predictions[-1])
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for idx, prediction in enumerate(predictions):
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self.print_latent_space(prediction, labels, running_index=idx, **kwargs)
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def print_latent_space(self, prediction, labels, running_index=0, save=None):
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self.colormap = cmaps.tab20
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if isinstance(prediction, torch.Tensor):
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prediction = prediction.numpy()
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elif isinstance(prediction, np.ndarray):
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pass
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elif isinstance(prediction, list):
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prediction = np.asarray(prediction)
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else:
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raise RuntimeError
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if prediction.shape[-1] > 2:
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fig, axs = plt.subplots(ncols=2, nrows=1)
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transformers = [TSNE(2), PCA(2)]
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print('Starting Dimensional Reduction')
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for idx, transformer in enumerate(transformers):
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transformed = transformer.fit_transform(prediction)
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print(f'{transformer.__class__.__name__} Projection Sucessfull')
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colored = self.colormap(labels)
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ax = axs[idx]
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ax.scatter(x=transformed[:, 0], y=transformed[:, 1], c=colored)
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ax.set_title(transformer.__class__.__name__)
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ax.set_xlim(np.min(transformed[:, 0])*1.1, np.max(transformed[:, 0]*1.1))
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ax.set_ylim(np.min(transformed[:, 1]*1.1), np.max(transformed[:, 1]*1.1))
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elif prediction.shape[-1] == 2:
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fig, axs = plt.subplots()
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# TODO: Build transformation for lat_dim_size >= 3
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print('All Predictions sucesfully Gathered and Shaped ')
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axs.set_xlim(np.min(prediction[:, 0]), np.max(prediction[:, 0]))
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axs.set_ylim(np.min(prediction[:, 1]), np.max(prediction[:, 1]))
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# ToDo: Insert Normalization
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colored = self.colormap(labels)
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plt.scatter(prediction[:, 0], prediction[:, 1], c=colored)
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else:
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raise NotImplementedError("Latent Dimensions can not be one-dimensional (yet).")
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if save:
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plt.savefig(f'{save}_{running_index}.png')
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def print_latent_density(self): # , data: DataContainer):
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raise NotImplementedError("My Future Self has to come up with smth")
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# fig, ax = plt.subplots()
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# preds = []
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# for i in range(data.len - data.width * data.stepsize):
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# for i in range(5000):
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#
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# seq = data.sub_trajectory_by_key(i, stepsize=data.stepsize)
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#
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# preds.append(self.nn.encoder([seq[None, ...]])[0])
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#
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# TODO: Build transformation for lat_dim_size >= 3
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# pred_array = np.asarray(preds).reshape((-1, nn.latDim))
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# k = KernelDensity()
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# k.fit(pred_array)
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# z = np.exp(k.score_samples(pred_array))
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#
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# levels = np.linspace(0, z.max(), 25)
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# xgrid, ygrid = np.meshgrid(pred_array[::5, 0], pred_array[::5, 1])
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# xy = np.vstack([xgrid.ravel(), ygrid.ravel()]).T
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# z = np.exp(k.score_samples(xy)).reshape(xgrid.shape)
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#
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# plt.contourf(xgrid, ygrid, z, levels=levels, cmap=plt.cm.Reds)
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# plt.show()
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def _gather_predictions(self, data: Trajectories, n: int = 1000,
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color_by_movement=False, **kwargs):
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"""
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Check if any value for n is given and gather some random datapoints from the dataset. In accordance with the
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maximal possible trajectory amount that is given by stepsize * width.
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Also retunr the keys for all possible predictions.
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:param data:
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:type data: Dataset
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:param n:
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:param tsne:
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:param kwargs:
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:return:
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"""
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print("Gathering Predictions")
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n = n if isinstance(n, int) and n else len(data) - (data.size * data.step)
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idxs = np.random.choice(np.arange(len(data) - data.step * data.size), n, replace=False)
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complete_data = torch.stack([data.get_both_by_key(idx) for idx in idxs], dim=0)
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segment_coords, trajectories = complete_data[:, :, :2], complete_data[:, :, 2:]
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if color_by_movement:
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motion_analyser = MotionAnalyser()
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predictions = (motion_analyser.cluster_motion(segment_coords), )
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else:
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with torch.no_grad():
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predictions = self.network(trajectories)[:-1]
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return predictions, segment_coords
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@staticmethod
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def colorize_as_hsv(self, x, min_val: Union[float, None] = None, max_val: Union[float, None] = None,
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colormap=cmaps.rainbow, **kwargs):
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norm = mcolors.Normalize(vmin=min_val, vmax=max_val)
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colored = colormap(norm(x))
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return colored
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def _build_trajectory_shapes(self, predictions: np.ndarray, segment_coordinates,
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axis=None, transformation=TSNE, **kwargs):
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if not isinstance(predictions, np.ndarray):
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predictions = tuple((x if torch.is_tensor(x) else torch.from_numpy(x) for x in predictions))
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predictions = torch.cat(predictions, dim=-1)
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if axis is not None:
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predictions = predictions[:, axis][..., None]
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if predictions.shape[-1] >= 4:
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if True:
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predictions = Birch(n_clusters=3).fit_predict(predictions).reshape(-1, 1)
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else:
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transformer = transformation(n_components=3, random_state=42)
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predictions = transformer.fit_transform(predictions)
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if predictions.shape[-1] == 1:
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colored = self.colorize(predictions.reshape(-1), **kwargs)
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elif predictions.shape[-1] == 2:
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colored = self.colorize(predictions[:, 0], **kwargs)
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if kwargs.get('min_val', None):
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lightning = mcolors.Normalize(vmin=kwargs.get('min_val', None), vmax=kwargs.get('max_val', None))
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else:
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lightning = mcolors.Normalize()
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alpha = lightning(predictions[:, 1])
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colored[:, -1] = alpha
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elif predictions.shape[-1] == 3:
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norm = mcolors.Normalize()
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colored = [(r, g, b) for r,g,b in norm(predictions)]
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else:
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raise NotImplementedError('Full Prediction Shape was: {}'.format(predictions.shape))
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# TODO Build a isomap or tsne transformation here to get a two dimensional space
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segment_coordinates = segment_coordinates.cpu() if torch.is_tensor(segment_coordinates) else segment_coordinates
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return LineCollection(segment_coordinates, linewidths=(1, 1, 1, 1),
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colors=colored, linestyle='solid')
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@staticmethod
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def _build_map_shapes(base_map: Map):
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# Base Map Plotting
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# filled Triangle
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patches = [Polygon(base_map[i], True, color='black') for i in range(len(base_map))]
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return PatchCollection(patches, color='black')
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def print_trajec_on_basemap(self, data, base_map: Map, save=False, color_by_movement=False, **kwargs):
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"""
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:rtype: object
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"""
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prediction_segments = self._gather_predictions(data, color_by_movement=color_by_movement, **kwargs)
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trajectory_shapes = self._build_trajectory_shapes(*prediction_segments, **kwargs)
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map_shapes = self._build_map_shapes(base_map)
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self.ax.add_collection(trajectory_shapes)
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self.ax.axis('auto')
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self.ax.add_collection(map_shapes)
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self.ax.set_title('Trajectories on BaseMap')
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if save:
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if isinstance(save, str):
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self.save(save)
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else:
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self.save(base_map.name)
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pass
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@staticmethod
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def show():
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plt.show()
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return True
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@staticmethod
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def save(filename):
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plt.savefig(filename)
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class MotionAnalyser(object):
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def __init__(self):
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pass
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def _sequential_pairwise_map(self, func, xy_sequence, on_deltas=False):
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zipped_list = [x for x in zip(xy_sequence[:-1], xy_sequence[1:])]
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if on_deltas:
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zipped_list = [self.delta(*movement) for movement in zipped_list]
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else:
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pass
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return [func(*xy) for xy in zipped_list]
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@staticmethod
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def delta(x1y1, x2y2):
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x1, y1 = x1y1
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x2, y2 = x2y2
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return x2-x1, y2-y1
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@staticmethod
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def get_r(deltax, deltay):
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# https://mathinsight.org/polar_coordinates
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r = torch.sqrt(deltax**2 + deltay**2)
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return r
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@staticmethod
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def get_theta(deltax, deltay, rad=False):
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# https://mathinsight.org/polar_coordinates
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theta = torch.atan2(deltay, deltax)
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return theta if rad else theta * 180 / pi
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def get_theta_for_sequence(self, xy_sequence):
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ts = self._sequential_pairwise_map(self.get_theta, xy_sequence, on_deltas=True)
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return ts
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def get_r_for_sequence(self, xy_sequence):
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rs = self._sequential_pairwise_map(self.get_r, xy_sequence, on_deltas=True)
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return rs
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def get_unique_seq_identifier(self, xy_sequence):
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# Globals
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global_delta = self.delta(xy_sequence[0], xy_sequence[-1])
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global_theta = self.get_theta(*global_delta)
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global_r = self.get_r(*global_delta)
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# For Each
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theta_seq = self.get_theta_for_sequence(xy_sequence)
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mean_theta = sum(theta_seq) / len(theta_seq)
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theta_sum = sum([abs(theta) for theta in theta_seq])
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std_theta = stdev(map(float, theta_seq))
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return torch.stack((global_r, torch.as_tensor(std_theta), mean_theta, global_theta))
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def cluster_motion(self, trajectory_samples, cluster_class=KMeans):
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cluster_class = cluster_class(3)
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std, mean = torch.std_mean(trajectory_samples, dim=0)
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trajectory_samples = (trajectory_samples - mean) / std
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unique_seq_identifiers = torch.stack([self.get_unique_seq_identifier(trajectory)
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for trajectory in trajectory_samples])
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clustered_movement = cluster_class.fit_predict(unique_seq_identifiers)
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if False:
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from sklearn.decomposition import PCA
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p = PCA(2)
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t = p.fit_transform(unique_seq_identifiers)
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f = plt.figure()
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plt.scatter(t[:, 0], t[:,1])
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plt.show()
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return clustered_movement.reshape(-1, 1)
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if __name__ == '__main__':
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raise PermissionError('This file should not be called.')
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@ -1,12 +1,12 @@
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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
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import matplotlib.pyplot as plt
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import warnings
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warnings.filterwarnings('ignore', category=FutureWarning)
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import torch
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from dataset import DataContainer
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from viz.utils import search_for_weights, Printer
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from run_models import *
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sns.set()
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device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
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def load_and_predict(path_like_element):
|
||||
@ -14,73 +14,39 @@ def load_and_predict(path_like_element):
|
||||
splitpath = path_like_element.split(os.sep)
|
||||
base_dir = os.path.join(*splitpath[:4])
|
||||
model = globals()[splitpath[2]]
|
||||
print(f'... loading model named: "{Model.name}" from timestamp: {splitpath[3]}')
|
||||
pretrained_model = model.load_from_metrics(
|
||||
weights_path=path_like_element,
|
||||
tags_csv=os.path.join(base_dir, 'default', 'version_0', 'meta_tags.csv'),
|
||||
on_gpu=True if torch.cuda.is_available() else False,
|
||||
map_location=None
|
||||
# map_location=None
|
||||
)
|
||||
print(f'... loading model named: "{model.name}" from timestamp: {splitpath[3]}')
|
||||
|
||||
# Init model and freeze its weights ( for faster inference)
|
||||
pretrained_model = pretrained_model.to(device)
|
||||
pretrained_model.eval()
|
||||
pretrained_model.freeze()
|
||||
|
||||
with torch.no_grad():
|
||||
# Load the data for prediction
|
||||
|
||||
# Load the data for prediction
|
||||
# TODO!!!!!!!!!:
|
||||
# Hier müssen natürlich auch die date parameter geladen werden!
|
||||
# Muss ich die val-sets automatisch neu setzen, also immer auf refresh haben, wenn ich validieren möchte?
|
||||
# Was ist denn eigentlich mein Val Dataset?
|
||||
# Hab ich irgendwo eine ganze karte?
|
||||
# Wie sorge ich dafür, dass gewisse karten, also größenverhältnisse usw nicht überrepräsentiert sind?
|
||||
dataset = DataContainer(os.path.join(os.pardir, 'data', 'validation'), 9, 6).to(device)
|
||||
|
||||
# TODO!!!!!!!!!:
|
||||
# Hier müssen natürlich auch die date parameter geladen werden!
|
||||
# Muss ich die val-sets automatisch neu setzen, also immer auf refresh haben, wenn ich validieren möchte?
|
||||
# Was ist denn eigentlich mein Val Dataset?
|
||||
# Hab ich irgendwo eine ganze karte?
|
||||
# Wie sorge ich dafür, dass gewisse karten, also größenverhältnisse usw nicht überrepräsentiert sind?
|
||||
dataset = DataContainer(os.path.join(os.pardir, 'data', 'validation'), 9, 6).to(device)
|
||||
dataloader = DataLoader(dataset, shuffle=True, batch_size=len(dataset))
|
||||
# Do the inference
|
||||
# test_pred = [pretrained_model(test_sample)[:-1] for test_sample in dataloader][0]
|
||||
|
||||
# Do the inference
|
||||
test_pred = [pretrained_model(test_sample)[:-1] for test_sample in dataloader][0]
|
||||
|
||||
for idx, prediction in enumerate(test_pred):
|
||||
plot, _ = viz_latent(prediction)
|
||||
plot.savefig(os.path.join(base_dir, f'latent_space_{idx}.png'))
|
||||
|
||||
|
||||
def viz_latent(prediction):
|
||||
try:
|
||||
prediction = prediction.cpu()
|
||||
prediction = prediction.numpy()
|
||||
except AttributeError:
|
||||
pass
|
||||
|
||||
if prediction.shape[-1] <= 1:
|
||||
raise ValueError('How did this happen?')
|
||||
elif prediction.shape[-1] == 2:
|
||||
ax = sns.scatterplot(x=prediction[:, 0], y=prediction[:, 1])
|
||||
try:
|
||||
plt.show()
|
||||
except:
|
||||
pass
|
||||
return ax.figure, (ax)
|
||||
else:
|
||||
fig, axs = plt.subplots(ncols=2)
|
||||
plots = []
|
||||
for idx, dim_reducer in enumerate([PCA, TSNE]):
|
||||
predictions_reduced = dim_reducer(n_components=2).fit_transform(prediction)
|
||||
plot = sns.scatterplot(x=predictions_reduced[:, 0], y=predictions_reduced[:, 1],
|
||||
ax=axs[idx])
|
||||
plot.set_title(dim_reducer.__name__)
|
||||
plots.append(plot)
|
||||
|
||||
try:
|
||||
plt.show()
|
||||
except:
|
||||
pass
|
||||
return fig, (*plots, )
|
||||
p = Printer(pretrained_model)
|
||||
# Important:
|
||||
# Use all given valdiation samples, even if they relate to differnt maps. This is important since we want to have a
|
||||
# view on the complete latent space, not just in relation to a single basemap, which would be a major bias.
|
||||
p.print_possible_latent_spaces(dataset, save=os.path.join(base_dir, f'latent_space'))
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
path = 'output'
|
||||
search_for_weights(search_for_weights, path)
|
||||
search_for_weights(load_and_predict, path, file_type='latent')
|
||||
@ -1,50 +0,0 @@
|
||||
|
||||
from dataset import *
|
||||
# Plotting
|
||||
# import matplotlib as mlp
|
||||
from matplotlib import pyplot as plt
|
||||
from matplotlib.patches import Polygon
|
||||
from matplotlib.collections import LineCollection, PatchCollection
|
||||
import matplotlib.colors as mcolors
|
||||
import matplotlib.cm as cmaps
|
||||
|
||||
from sklearn.manifold import TSNE
|
||||
from sklearn.decomposition import PCA
|
||||
|
||||
import seaborn as sns
|
||||
from argparse import ArgumentParser
|
||||
|
||||
from viz.utils import search_for_weights
|
||||
|
||||
from run_models import *
|
||||
|
||||
sns.set()
|
||||
|
||||
|
||||
arguments = ArgumentParser()
|
||||
arguments.add_argument('--data', default=os.path.join('data', 'validation'))
|
||||
|
||||
dataset = DataContainer(os.path.join(os.pardir, 'data', 'validation'), 9, 6).to(device)
|
||||
dataloader = DataLoader(dataset, shuffle=True, batch_size=len(dataset))
|
||||
|
||||
|
||||
|
||||
def viz_map(self, base_map: MapContainer):
|
||||
# Base Map Plotting
|
||||
# filled Triangle
|
||||
patches = [Polygon(base_map.get_triangle_by_key(i), True, color='k') for i in range(len(base_map))]
|
||||
patch_collection = PatchCollection(patches, color='k')
|
||||
|
||||
self.ax.add_collection(patch_collection)
|
||||
print('Basemap Plotted')
|
||||
|
||||
patches = [Polygon(base_map.get_triangle_by_key(i), True, color='k') for i in range(len(base_map))]
|
||||
return PatchCollection(patches, color='k')
|
||||
|
||||
def load_and_predict(folder):
|
||||
pass
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
search_for_weights(load_and_predict, arguments.data)
|
||||
# ToDo: THIS
|
||||
55
viz/viz_prediction_in_map.py
Normal file
55
viz/viz_prediction_in_map.py
Normal file
@ -0,0 +1,55 @@
|
||||
from argparse import ArgumentParser
|
||||
import os
|
||||
|
||||
from dataset import DataContainer
|
||||
from viz.utils import MotionAnalyser, Printer, MapContainer, search_for_weights
|
||||
import torch
|
||||
|
||||
arguments = ArgumentParser()
|
||||
arguments.add_argument('--data', default='output')
|
||||
|
||||
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
|
||||
|
||||
from viz.utils import *
|
||||
from run_models import *
|
||||
|
||||
arguments = ArgumentParser()
|
||||
arguments.add_argument('--data', default='output')
|
||||
|
||||
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
|
||||
|
||||
|
||||
def load_and_viz(path_like_element):
|
||||
# Define Loop to search for models and folder with visualizations
|
||||
splitpath = path_like_element.split(os.sep)
|
||||
base_dir = os.path.join(*splitpath[:4])
|
||||
model = globals()[splitpath[2]]
|
||||
print(f'... loading model named: "{model.name}" from timestamp: {splitpath[3]}')
|
||||
pretrained_model = model.load_from_metrics(
|
||||
weights_path=path_like_element,
|
||||
tags_csv=os.path.join(base_dir, 'default', 'version_0', 'meta_tags.csv'),
|
||||
on_gpu=True if torch.cuda.is_available() else False,
|
||||
# map_location=None
|
||||
)
|
||||
|
||||
# Init model and freeze its weights ( for faster inference)
|
||||
pretrained_model = pretrained_model.to(device)
|
||||
pretrained_model.eval()
|
||||
pretrained_model.freeze()
|
||||
|
||||
dataIndex = 0
|
||||
|
||||
datasets = DataContainer(os.path.join(os.pardir, 'data', 'validation'), 9, 6).to(device)
|
||||
dataset = datasets.datasets[dataIndex]
|
||||
# ToDO: use dataloader for iteration instead! - dataloader = DataLoader(dataset, )
|
||||
|
||||
maps = MapContainer(os.path.join(os.pardir, 'data', 'validation'))
|
||||
base_map = maps.datasets[dataIndex]
|
||||
|
||||
p = Printer(pretrained_model)
|
||||
p.print_trajec_on_basemap(dataset, base_map, save=os.path.join(base_dir, f'{base_map.name}_map.png'))
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
args = arguments.parse_args()
|
||||
search_for_weights(load_and_viz, args.data, file_type='map')
|
||||
Reference in New Issue
Block a user