Offline Datasets res net optionality
This commit is contained in:
Si11ium
parent
2f99341cc3
commit
bb47e07566
@ -6,7 +6,7 @@ import torch
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from torch.utils.data import Dataset, ConcatDataset
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from datasets.utils import DatasetMapping
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from lib.modules.model_parts import Generator
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from lib.preprocessing.generator import Generator
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from lib.objects.map import Map
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@ -2,15 +2,50 @@ import shelve
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from pathlib import Path
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from typing import Union, List
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import multiprocessing as mp
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import torch
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from random import choice
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from torch.utils.data import ConcatDataset, Dataset
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import numpy as np
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from tqdm import tqdm
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from lib.objects.map import Map
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import lib.variables as V
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from PIL import Image
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from lib.utils.tools import write_to_shelve
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class TrajDataShelve(Dataset):
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@property
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def map_shape(self):
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return self[0][0].shape
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def __init__(self, file_path, **kwargs):
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super(TrajDataShelve, self).__init__()
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self._mutex = mp.Lock()
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self.file_path = str(file_path)
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def __len__(self):
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self._mutex.acquire()
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with shelve.open(self.file_path) as d:
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length = len(d)
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self._mutex.release()
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return length
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def seed(self):
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pass
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def __getitem__(self, item):
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self._mutex.acquire()
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with shelve.open(self.file_path) as d:
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sample = d[str(item)]
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self._mutex.release()
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return sample
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class TrajDataset(Dataset):
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@ -22,14 +57,15 @@ class TrajDataset(Dataset):
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length=100000, mode='separated_arrays', embedding_size=None, preserve_equal_samples=False,
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**kwargs):
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super(TrajDataset, self).__init__()
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assert mode.lower() in ['vectors', 'all_in_map', 'separated_arrays', 'just_route']
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assert mode.lower() in ['generator_all_in_map', 'generator_hom_all_in_map'
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'classifier_all_in_map']
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self.normalized = normalized
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self.preserve_equal_samples = preserve_equal_samples
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self.mode = mode
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self.mapname = mapname if mapname.endswith('.bmp') else f'{mapname}.bmp'
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self.maps_root = maps_root
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self._len = length
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self.last_label = -1
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self.last_label = V.ALTERNATIVE if 'hom' in self.mode else choice([-1, V.ALTERNATIVE, V.HOMOTOPIC])
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self.map = Map(self.mapname).from_image(self.maps_root / self.mapname, embedding_size=embedding_size)
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@ -39,6 +75,7 @@ class TrajDataset(Dataset):
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def __getitem__(self, item):
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if self.mode.lower() == 'just_route':
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raise NotImplementedError
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trajectory = self.map.get_random_trajectory()
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trajectory_space = trajectory.draw_in_array(self.map.shape)
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label = choice([0, 1])
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@ -54,37 +91,41 @@ class TrajDataset(Dataset):
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else:
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break
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self.last_label = label
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if self.mode.lower() in ['all_in_map', 'separated_arrays']:
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self.last_label = label if self.mode != ['generator_hom_all_in_map'] else V.ALTERNATIVE
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if self.mode.lower() in ['classifier_all_in_map', 'generator_all_in_map']:
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map_array = self.map.as_array
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trajectory = trajectory.draw_in_array(self.map_shape)
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alternative = alternative.draw_in_array(self.map_shape)
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if self.mode == 'separated_arrays':
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if self.normalized:
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map_array = map_array / V.WHITE
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trajectory = trajectory / V.WHITE
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alternative = alternative / V.WHITE
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return (map_array, trajectory, label), alternative
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else:
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label_as_array = np.full_like(map_array, label)
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if self.normalized:
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map_array = map_array / V.WHITE
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trajectory = trajectory / V.WHITE
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alternative = alternative / V.WHITE
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if self.mode == 'generator_all_in_map':
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return np.concatenate((map_array, trajectory, label_as_array)), alternative
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elif self.mode == 'classifier_all_in_map':
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return np.concatenate((map_array, trajectory, alternative)), label
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elif self.mode == 'vectors':
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elif self.mode == '_vectors':
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raise NotImplementedError
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return trajectory.vertices, alternative.vertices, label, self.mapname
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else:
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raise ValueError
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raise ValueError(f'Mode was: {self.mode}')
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def seed(self, seed):
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self.map.seed(seed)
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class TrajData(object):
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@property
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def map_shapes(self):
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return [dataset.map_shape for dataset in self._dataset.datasets]
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return [dataset.map_shape for dataset in self._train_dataset.datasets]
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@property
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def map_shapes_max(self):
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shapes = self.map_shapes
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shape_list = list(map(max, zip(*shapes)))
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if self.mode in ['separated_arrays', 'all_in_map']:
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if '_all_in_map' in self.mode:
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shape_list[0] += 2
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return shape_list
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@ -92,36 +133,81 @@ class TrajData(object):
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def name(self):
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return self.__class__.__name__
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def __init__(self, map_root, length=100000, mode='separated_arrays', normalized=True, **_):
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def __init__(self, map_root, length=100000, mode='separated_arrays', normalized=True, preprocessed=False, **_):
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self.preprocessed = preprocessed
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self.normalized = normalized
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self.mode = mode
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self.maps_root = Path(map_root)
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self.length = length
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self._dataset = self._load_datasets()
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self._test_dataset = self._load_datasets('train')
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self._val_dataset = self._load_datasets('val')
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self._train_dataset = self._load_datasets('test')
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def _load_datasets(self, dataset_type=''):
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def _load_datasets(self):
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map_files = list(self.maps_root.glob('*.bmp'))
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equal_split = int(self.length // len(map_files)) or 1
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# find max image size among available maps:
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max_map_size = (1, ) + tuple(reversed(tuple(map(max, *[Image.open(map_file).size for map_file in map_files]))))
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if self.preprocessed:
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preprocessed_map_files = list(self.maps_root.glob('*.pik'))
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preprocessed_map_names = [p.name for p in preprocessed_map_files]
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datasets = []
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for map_file in map_files:
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new_pik_name = f'{dataset_type}_{str(map_file.name)[:-3]}.pik'
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if dataset_type != 'train':
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equal_split *= 0.01
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if not [f'{new_pik_name[:-3]}.bmp' in preprocessed_map_names]:
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traj_dataset = TrajDataset(maps_root=self.maps_root, mapname=map_file.name, length=equal_split,
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mode=self.mode, embedding_size=max_map_size, normalized=self.normalized,
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preserve_equal_samples=True)
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self.dump_n(map_file.parent / new_pik_name, traj_dataset, n=equal_split)
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dataset = TrajDataShelve(map_file.parent / new_pik_name)
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datasets.append(dataset)
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return ConcatDataset(datasets)
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return ConcatDataset([TrajDataset(maps_root=self.maps_root, mapname=map_file.name, length=equal_split,
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mode=self.mode, embedding_size=max_map_size, normalized=self.normalized,
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preserve_equal_samples=True)
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for map_file in map_files])
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def kill_em_all(self):
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for pik_file in self.maps_root.glob('*.pik'):
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pik_file.unlink()
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print(pik_file.name, ' was deleted.')
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print('Done.')
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def seed(self, seed):
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for dataset in [x.datasets for x in [self._train_dataset, self._test_dataset, self.val_dataset]]:
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dataset.seed(seed)
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def dump_n(self, file_path, traj_dataset: TrajDataset, n=100000):
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assert str(file_path).endswith('.pik')
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processes = mp.cpu_count() - 1
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mutex = mp.Lock()
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with mp.Pool(processes) as pool:
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async_results = [pool.apply_async(traj_dataset.__getitem__, kwds=dict(item=i)) for i in range(n)]
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for result_obj in tqdm(async_results, total=n, desc=f'Generating {n} Samples'):
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sample = result_obj.get()
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mutex.acquire()
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write_to_shelve(file_path, sample)
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mutex.release()
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print(f'{n} samples sucessfully dumped to "{file_path}"!')
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@property
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def train_dataset(self):
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return self._dataset
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return self._train_dataset
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@property
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def val_dataset(self):
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return self._dataset
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return self._val_dataset
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@property
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def test_dataset(self):
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return self._dataset
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return self._test_dataset
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def get_datasets(self):
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return self._dataset, self._dataset, self._dataset
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return self._train_dataset, self._val_dataset, self._test_dataset
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@ -1,4 +1,5 @@
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from random import choice
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from random import choices, seed
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import numpy as np
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import torch
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from functools import reduce
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@ -36,28 +37,36 @@ class CNNRouteGeneratorModel(LightningBaseModule):
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# kld_loss /= reduce(mul, self.in_shape)
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# kld_loss *= self.hparams.data_param.dataset_length / self.hparams.train_param.batch_size * 100
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loss = (kld_loss + element_wise_loss) / 2
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loss = kld_loss + element_wise_loss
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return dict(loss=loss, log=dict(element_wise_loss=element_wise_loss, loss=loss, kld_loss=kld_loss))
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def _test_val_step(self, batch_xy, batch_nb, *args):
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batch_x, _ = batch_xy
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map_array, trajectory, label = batch_x
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map_array = batch_x[:, 0].unsqueeze(1)
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trajectory = batch_x[:, 1].unsqueeze(1)
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labels = batch_x[:, 2].unsqueeze(1).max(dim=-1).values.max(-1).values
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generated_alternative, z, mu, logvar = self(batch_x)
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return dict(batch_nb=batch_nb, label=label, generated_alternative=generated_alternative, pred_label=-1)
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_, mu, _ = self.encode(batch_x)
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generated_alternative = self.generate(mu)
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return dict(maps=map_array, trajectories=trajectory, batch_nb=batch_nb, labels=labels,
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generated_alternative=generated_alternative, pred_label=-1)
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def _test_val_epoch_end(self, outputs, test=False):
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maps, trajectories, labels, val_restul_dict = self.generate_random()
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val_restul_dict = self.generate_random()
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from lib.visualization.generator_eval import GeneratorVisualizer
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g = GeneratorVisualizer(maps, trajectories, labels, val_restul_dict)
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g = GeneratorVisualizer(**val_restul_dict)
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fig = g.draw()
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self.logger.log_image(f'{self.name}_Output', fig, step=self.global_step)
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plt.clf()
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return dict(epoch=self.current_epoch)
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def on_epoch_start(self):
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self.dataset.seed(self.logger.version)
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# torch.random.manual_seed(self.logger.version)
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# np.random.seed(self.logger.version)
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def validation_step(self, *args):
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return self._test_val_step(*args)
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@ -75,14 +84,18 @@ class CNNRouteGeneratorModel(LightningBaseModule):
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if not issubclassed:
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# Dataset
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self.dataset = TrajData(self.hparams.data_param.map_root, mode='separated_arrays',
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self.dataset = TrajData(self.hparams.data_param.map_root, mode='generator_all_in_map',
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preprocessed=self.hparams.data_param.use_preprocessed,
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length=self.hparams.data_param.dataset_length, normalized=True)
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self.criterion = nn.MSELoss()
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# Additional Attributes
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# Additional Attributes #
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#######################################################
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self.in_shape = self.dataset.map_shapes_max
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# Todo: Better naming and size in Parameters
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self.feature_dim = self.hparams.model_param.lat_dim * 10
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self.use_res_net = self.hparams.model_param.use_res_net
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self.lat_dim = self.hparams.model_param.lat_dim
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self.feature_dim = self.lat_dim * 10
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########################################################
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# NN Nodes
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###################################################
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@ -93,82 +106,100 @@ class CNNRouteGeneratorModel(LightningBaseModule):
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#
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# Map Encoder
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self.map_conv_0 = ConvModule(self.in_shape, conv_kernel=3, conv_stride=1, conv_padding=1,
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self.enc_conv_0 = ConvModule(self.in_shape, conv_kernel=3, conv_stride=1, conv_padding=1,
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conv_filters=self.hparams.model_param.filters[0],
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use_norm=self.hparams.model_param.use_norm,
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use_bias=self.hparams.model_param.use_bias)
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self.map_res_1 = ResidualModule(self.map_conv_0.shape, ConvModule, 2, conv_kernel=5, conv_stride=1,
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self.enc_res_1 = ResidualModule(self.enc_conv_0.shape, ConvModule, 2, conv_kernel=5, conv_stride=1,
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conv_padding=2, conv_filters=self.hparams.model_param.filters[0],
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use_norm=self.hparams.model_param.use_norm,
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use_bias=self.hparams.model_param.use_bias)
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self.map_conv_1 = ConvModule(self.map_res_1.shape, conv_kernel=5, conv_stride=1, conv_padding=0,
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conv_filters=self.hparams.model_param.filters[1],
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use_norm=self.hparams.model_param.use_norm,
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use_bias=self.hparams.model_param.use_bias)
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self.enc_conv_1a = ConvModule(self.enc_res_1.shape, conv_kernel=3, conv_stride=1, conv_padding=0,
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conv_filters=self.hparams.model_param.filters[1],
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use_norm=self.hparams.model_param.use_norm,
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use_bias=self.hparams.model_param.use_bias)
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self.enc_conv_1b = ConvModule(self.enc_conv_1a.shape, conv_kernel=3, conv_stride=2, conv_padding=0,
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conv_filters=self.hparams.model_param.filters[1],
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use_norm=self.hparams.model_param.use_norm,
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use_bias=self.hparams.model_param.use_bias)
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self.map_res_2 = ResidualModule(self.map_conv_1.shape, ConvModule, 2, conv_kernel=5, conv_stride=1,
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self.enc_res_2 = ResidualModule(self.enc_conv_1b.shape, ConvModule, 2, conv_kernel=5, conv_stride=1,
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conv_padding=2, conv_filters=self.hparams.model_param.filters[1],
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use_norm=self.hparams.model_param.use_norm,
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use_bias=self.hparams.model_param.use_bias)
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self.map_conv_2 = ConvModule(self.map_res_2.shape, conv_kernel=7, conv_stride=1, conv_padding=0,
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conv_filters=self.hparams.model_param.filters[2],
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use_norm=self.hparams.model_param.use_norm,
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use_bias=self.hparams.model_param.use_bias)
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self.enc_conv_2a = ConvModule(self.enc_res_2.shape, conv_kernel=5, conv_stride=1, conv_padding=0,
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conv_filters=self.hparams.model_param.filters[2],
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use_norm=self.hparams.model_param.use_norm,
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use_bias=self.hparams.model_param.use_bias)
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self.enc_conv_2b = ConvModule(self.enc_conv_2a.shape, conv_kernel=5, conv_stride=1, conv_padding=0,
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conv_filters=self.hparams.model_param.filters[2],
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use_norm=self.hparams.model_param.use_norm,
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use_bias=self.hparams.model_param.use_bias)
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self.map_res_3 = ResidualModule(self.map_conv_2.shape, ConvModule, 2, conv_kernel=7, conv_stride=1,
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self.enc_res_3 = ResidualModule(self.enc_conv_2b.shape, ConvModule, 2, conv_kernel=7, conv_stride=1,
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conv_padding=3, conv_filters=self.hparams.model_param.filters[2],
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use_norm=self.hparams.model_param.use_norm,
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use_bias=self.hparams.model_param.use_bias)
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self.map_conv_3 = ConvModule(self.map_res_3.shape, conv_kernel=11, conv_stride=1, conv_padding=0,
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conv_filters=self.hparams.model_param.filters[2],
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use_norm=self.hparams.model_param.use_norm,
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use_bias=self.hparams.model_param.use_bias)
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self.enc_conv_3a = ConvModule(self.enc_res_3.shape, conv_kernel=7, conv_stride=1, conv_padding=0,
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conv_filters=self.hparams.model_param.filters[2],
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use_norm=self.hparams.model_param.use_norm,
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use_bias=self.hparams.model_param.use_bias)
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self.enc_conv_3b = ConvModule(self.enc_conv_3a.shape, conv_kernel=7, conv_stride=1, conv_padding=0,
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conv_filters=self.hparams.model_param.filters[2],
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use_norm=self.hparams.model_param.use_norm,
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use_bias=self.hparams.model_param.use_bias)
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self.map_flat = Flatten(self.map_conv_3.shape)
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self.map_lin = nn.Linear(reduce(mul, self.map_conv_3.shape), self.feature_dim)
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self.enc_flat = Flatten(self.enc_conv_3b.shape)
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self.enc_lin_1 = nn.Linear(self.enc_flat.shape, self.feature_dim)
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#
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# Mixed Encoder
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self.mixed_lin = nn.Linear(self.feature_dim, self.feature_dim)
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self.mixed_norm = nn.BatchNorm1d(self.feature_dim) if self.hparams.model_param.use_norm else lambda x: x
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self.enc_lin_2 = nn.Linear(self.feature_dim, self.feature_dim)
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self.enc_norm = nn.BatchNorm1d(self.feature_dim) if self.hparams.model_param.use_norm else lambda x: x
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#
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# Variational Bottleneck
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self.mu = nn.Linear(self.feature_dim, self.hparams.model_param.lat_dim)
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self.logvar = nn.Linear(self.feature_dim, self.hparams.model_param.lat_dim)
|
||||
self.mu = nn.Linear(self.feature_dim, self.lat_dim)
|
||||
self.logvar = nn.Linear(self.feature_dim, self.lat_dim)
|
||||
|
||||
#
|
||||
# Alternative Generator
|
||||
self.alt_lin_1 = nn.Linear(self.hparams.model_param.lat_dim, self.feature_dim)
|
||||
# Todo Fix This Hack!!!!
|
||||
reshape_shape = (1, self.map_conv_3.shape[1], self.map_conv_3.shape[2])
|
||||
self.gen_lin_1 = nn.Linear(self.hparams.model_param.lat_dim, self.feature_dim)
|
||||
|
||||
self.alt_lin_2 = nn.Linear(self.feature_dim, reduce(mul, reshape_shape))
|
||||
self.gen_lin_2 = nn.Linear(self.feature_dim, self.enc_flat.shape)
|
||||
|
||||
self.reshape_to_map = Flatten(reduce(mul, reshape_shape), reshape_shape)
|
||||
self.reshape_to_last_conv = Flatten(self.enc_flat.shape, self.enc_conv_3b.shape)
|
||||
|
||||
self.alt_deconv_1 = DeConvModule(reshape_shape, self.hparams.model_param.filters[2],
|
||||
conv_padding=0, conv_kernel=13, conv_stride=1,
|
||||
use_norm=self.hparams.model_param.use_norm)
|
||||
self.alt_deconv_2 = DeConvModule(self.alt_deconv_1.shape, self.hparams.model_param.filters[1],
|
||||
conv_padding=0, conv_kernel=7, conv_stride=1,
|
||||
use_norm=self.hparams.model_param.use_norm)
|
||||
self.alt_deconv_3 = DeConvModule(self.alt_deconv_2.shape, self.hparams.model_param.filters[0],
|
||||
conv_padding=1, conv_kernel=5, conv_stride=1,
|
||||
use_norm=self.hparams.model_param.use_norm)
|
||||
self.alt_deconv_out = DeConvModule(self.alt_deconv_3.shape, 1, activation=None,
|
||||
conv_padding=1, conv_kernel=3, conv_stride=1,
|
||||
self.gen_deconv_1a = DeConvModule(self.enc_conv_3b.shape, self.hparams.model_param.filters[2],
|
||||
conv_padding=0, conv_kernel=11, conv_stride=1,
|
||||
use_norm=self.hparams.model_param.use_norm)
|
||||
self.gen_deconv_1b = DeConvModule(self.gen_deconv_1a.shape, self.hparams.model_param.filters[2],
|
||||
conv_padding=0, conv_kernel=9, conv_stride=2,
|
||||
use_norm=self.hparams.model_param.use_norm)
|
||||
|
||||
self.gen_deconv_2a = DeConvModule(self.gen_deconv_1b.shape, self.hparams.model_param.filters[1],
|
||||
conv_padding=0, conv_kernel=7, conv_stride=1,
|
||||
use_norm=self.hparams.model_param.use_norm)
|
||||
self.gen_deconv_2b = DeConvModule(self.gen_deconv_2a.shape, self.hparams.model_param.filters[1],
|
||||
conv_padding=0, conv_kernel=7, conv_stride=1,
|
||||
use_norm=self.hparams.model_param.use_norm)
|
||||
|
||||
self.gen_deconv_3a = DeConvModule(self.gen_deconv_2b.shape, self.hparams.model_param.filters[0],
|
||||
conv_padding=1, conv_kernel=5, conv_stride=1,
|
||||
use_norm=self.hparams.model_param.use_norm)
|
||||
self.gen_deconv_3b = DeConvModule(self.gen_deconv_3a.shape, self.hparams.model_param.filters[0],
|
||||
conv_padding=1, conv_kernel=4, conv_stride=1,
|
||||
use_norm=self.hparams.model_param.use_norm)
|
||||
|
||||
self.gen_deconv_out = DeConvModule(self.gen_deconv_3b.shape, 1, activation=None,
|
||||
conv_padding=0, conv_kernel=3, conv_stride=1,
|
||||
use_norm=self.hparams.model_param.use_norm)
|
||||
|
||||
def forward(self, batch_x):
|
||||
#
|
||||
# Sorting the Input
|
||||
map_array, trajectory, label = batch_x
|
||||
|
||||
#
|
||||
# Encode
|
||||
z, mu, logvar = self.encode(map_array, trajectory, label)
|
||||
z, mu, logvar = self.encode(batch_x)
|
||||
|
||||
#
|
||||
# Generate
|
||||
@ -181,42 +212,26 @@ class CNNRouteGeneratorModel(LightningBaseModule):
|
||||
eps = torch.randn_like(std)
|
||||
return mu + eps * std
|
||||
|
||||
def generate(self, z):
|
||||
alt_tensor = self.alt_lin_1(z)
|
||||
alt_tensor = self.activation(alt_tensor)
|
||||
alt_tensor = self.alt_lin_2(alt_tensor)
|
||||
alt_tensor = self.activation(alt_tensor)
|
||||
alt_tensor = self.reshape_to_map(alt_tensor)
|
||||
alt_tensor = self.alt_deconv_1(alt_tensor)
|
||||
alt_tensor = self.alt_deconv_2(alt_tensor)
|
||||
alt_tensor = self.alt_deconv_3(alt_tensor)
|
||||
alt_tensor = self.alt_deconv_out(alt_tensor)
|
||||
# alt_tensor = self.activation(alt_tensor)
|
||||
alt_tensor = self.sigmoid(alt_tensor)
|
||||
return alt_tensor
|
||||
def encode(self, batch_x):
|
||||
combined_tensor = self.enc_conv_0(batch_x)
|
||||
combined_tensor = self.enc_res_1(combined_tensor) if self.use_res_net else combined_tensor
|
||||
combined_tensor = self.enc_conv_1a(combined_tensor)
|
||||
combined_tensor = self.enc_conv_1b(combined_tensor)
|
||||
combined_tensor = self.enc_res_2(combined_tensor) if self.use_res_net else combined_tensor
|
||||
combined_tensor = self.enc_conv_2a(combined_tensor)
|
||||
combined_tensor = self.enc_conv_2b(combined_tensor)
|
||||
combined_tensor = self.enc_res_3(combined_tensor) if self.use_res_net else combined_tensor
|
||||
combined_tensor = self.enc_conv_3a(combined_tensor)
|
||||
combined_tensor = self.enc_conv_3b(combined_tensor)
|
||||
|
||||
def encode(self, map_array, trajectory, label):
|
||||
label_array = torch.cat([torch.full((1, 1, self.in_shape[1], self.in_shape[2]), x.item())
|
||||
for x in label], dim=0)
|
||||
label_array = self._move_to_model_device(label_array)
|
||||
combined_tensor = torch.cat((map_array, trajectory, label_array), dim=1)
|
||||
combined_tensor = self.map_conv_0(combined_tensor)
|
||||
combined_tensor = self.map_res_1(combined_tensor)
|
||||
combined_tensor = self.map_conv_1(combined_tensor)
|
||||
combined_tensor = self.map_res_2(combined_tensor)
|
||||
combined_tensor = self.map_conv_2(combined_tensor)
|
||||
combined_tensor = self.map_res_3(combined_tensor)
|
||||
combined_tensor = self.map_conv_3(combined_tensor)
|
||||
combined_tensor = self.enc_flat(combined_tensor)
|
||||
combined_tensor = self.enc_lin_1(combined_tensor)
|
||||
combined_tensor = self.enc_lin_2(combined_tensor)
|
||||
|
||||
combined_tensor = self.map_flat(combined_tensor)
|
||||
combined_tensor = self.map_lin(combined_tensor)
|
||||
|
||||
combined_tensor = self.mixed_lin(combined_tensor)
|
||||
|
||||
combined_tensor = self.mixed_norm(combined_tensor)
|
||||
combined_tensor = self.enc_norm(combined_tensor)
|
||||
combined_tensor = self.activation(combined_tensor)
|
||||
combined_tensor = self.mixed_lin(combined_tensor)
|
||||
combined_tensor = self.mixed_norm(combined_tensor)
|
||||
combined_tensor = self.enc_lin_2(combined_tensor)
|
||||
combined_tensor = self.enc_norm(combined_tensor)
|
||||
combined_tensor = self.activation(combined_tensor)
|
||||
|
||||
#
|
||||
@ -226,19 +241,31 @@ class CNNRouteGeneratorModel(LightningBaseModule):
|
||||
z = self.reparameterize(mu, logvar)
|
||||
return z, mu, logvar
|
||||
|
||||
def generate_random(self, n=6):
|
||||
maps = [self.map_storage[choice(self.map_storage.keys_list)] for _ in range(n)]
|
||||
def generate(self, z):
|
||||
alt_tensor = self.gen_lin_1(z)
|
||||
alt_tensor = self.activation(alt_tensor)
|
||||
alt_tensor = self.gen_lin_2(alt_tensor)
|
||||
alt_tensor = self.activation(alt_tensor)
|
||||
alt_tensor = self.reshape_to_last_conv(alt_tensor)
|
||||
alt_tensor = self.gen_deconv_1a(alt_tensor)
|
||||
alt_tensor = self.gen_deconv_1b(alt_tensor)
|
||||
alt_tensor = self.gen_deconv_2a(alt_tensor)
|
||||
alt_tensor = self.gen_deconv_2b(alt_tensor)
|
||||
alt_tensor = self.gen_deconv_3a(alt_tensor)
|
||||
alt_tensor = self.gen_deconv_3b(alt_tensor)
|
||||
alt_tensor = self.gen_deconv_out(alt_tensor)
|
||||
# alt_tensor = self.activation(alt_tensor)
|
||||
alt_tensor = self.sigmoid(alt_tensor)
|
||||
return alt_tensor
|
||||
|
||||
trajectories = [x.get_random_trajectory() for x in maps]
|
||||
trajectories = [x.draw_in_array(self.map_storage.max_map_size) for x in trajectories]
|
||||
trajectories = [torch.as_tensor(x, dtype=torch.float32) for x in trajectories] * 2
|
||||
trajectories = self._move_to_model_device(torch.stack(trajectories))
|
||||
def generate_random(self, n=12):
|
||||
|
||||
maps = [torch.as_tensor(x.as_array, dtype=torch.float32) for x in maps] * 2
|
||||
maps = self._move_to_model_device(torch.stack(maps))
|
||||
samples, alternatives = zip(*[self.dataset.test_dataset[choice]
|
||||
for choice in choices(range(self.dataset.length), k=n)])
|
||||
samples = self._move_to_model_device(torch.stack([torch.as_tensor(x) for x in samples]))
|
||||
alternatives = self._move_to_model_device(torch.stack([torch.as_tensor(x) for x in alternatives]))
|
||||
|
||||
labels = self._move_to_model_device(torch.as_tensor([0] * n + [1] * n))
|
||||
return maps, trajectories, labels, self._test_val_step(((maps, trajectories, labels), None), -9999)
|
||||
return self._test_val_step((samples, alternatives), -9999)
|
||||
|
||||
|
||||
class CNNRouteGeneratorDiscriminated(CNNRouteGeneratorModel):
|
||||
@ -329,11 +356,12 @@ class CNNRouteGeneratorDiscriminated(CNNRouteGeneratorModel):
|
||||
self._disc = disc_model
|
||||
|
||||
def __init__(self, *params):
|
||||
raise NotImplementedError
|
||||
super(CNNRouteGeneratorDiscriminated, self).__init__(*params, issubclassed=True)
|
||||
|
||||
self._disc = None
|
||||
|
||||
self.criterion = nn.BCELoss()
|
||||
|
||||
self.dataset = TrajData(self.hparams.data_param.map_root, mode='just_route',
|
||||
self.dataset = TrajData(self.hparams.data_param.map_root, mode='just_route', preprocessed=True,
|
||||
length=self.hparams.data_param.dataset_length, normalized=True)
|
||||
|
||||
@ -0,0 +1,348 @@
|
||||
from random import choice
|
||||
|
||||
import torch
|
||||
from functools import reduce
|
||||
from operator import mul
|
||||
|
||||
from torch import nn
|
||||
from torch.optim import Adam
|
||||
|
||||
from datasets.trajectory_dataset import TrajData
|
||||
from lib.evaluation.classification import ROCEvaluation
|
||||
from lib.modules.blocks import ConvModule, ResidualModule, DeConvModule
|
||||
from lib.modules.utils import LightningBaseModule, Flatten
|
||||
|
||||
import matplotlib.pyplot as plt
|
||||
|
||||
|
||||
class CNNRouteGeneratorModel(LightningBaseModule):
|
||||
|
||||
name = 'CNNRouteGenerator'
|
||||
|
||||
def configure_optimizers(self):
|
||||
return Adam(self.parameters(), lr=self.hparams.train_param.lr)
|
||||
|
||||
def training_step(self, batch_xy, batch_nb, *args, **kwargs):
|
||||
batch_x, alternative = batch_xy
|
||||
generated_alternative, z, mu, logvar = self(batch_x)
|
||||
element_wise_loss = self.criterion(generated_alternative, alternative)
|
||||
# see Appendix B from VAE paper:
|
||||
# Kingma and Welling. Auto-Encoding Variational Bayes. ICLR, 2014
|
||||
# https://arxiv.org/abs/1312.6114
|
||||
# 0.5 * sum(1 + log(sigma^2) - mu^2 - sigma^2)
|
||||
|
||||
kld_loss = -0.5 * torch.sum(1 + logvar - mu.pow(2) - logvar.exp())
|
||||
# Dimensional Resizing TODO: Does This make sense? Sanity Check it!
|
||||
# kld_loss /= reduce(mul, self.in_shape)
|
||||
# kld_loss *= self.hparams.data_param.dataset_length / self.hparams.train_param.batch_size * 100
|
||||
|
||||
loss = (kld_loss + element_wise_loss) / 2
|
||||
return dict(loss=loss, log=dict(element_wise_loss=element_wise_loss, loss=loss, kld_loss=kld_loss))
|
||||
|
||||
def _test_val_step(self, batch_xy, batch_nb, *args):
|
||||
batch_x, alternative = batch_xy
|
||||
map_array = batch_x[0]
|
||||
trajectory = batch_x[1]
|
||||
label = batch_x[2].max()
|
||||
|
||||
z, _, _ = self.encode(batch_x)
|
||||
generated_alternative = self.generate(z)
|
||||
|
||||
return dict(map_array=map_array, trajectory=trajectory, batch_nb=batch_nb, label=label,
|
||||
generated_alternative=generated_alternative, pred_label=-1, alternative=alternative
|
||||
)
|
||||
|
||||
def _test_val_epoch_end(self, outputs, test=False):
|
||||
maps, trajectories, labels, val_restul_dict = self.generate_random()
|
||||
|
||||
from lib.visualization.generator_eval import GeneratorVisualizer
|
||||
g = GeneratorVisualizer(maps, trajectories, labels, val_restul_dict)
|
||||
fig = g.draw()
|
||||
self.logger.log_image(f'{self.name}_Output', fig, step=self.global_step)
|
||||
plt.clf()
|
||||
|
||||
return dict(epoch=self.current_epoch)
|
||||
|
||||
def validation_step(self, *args):
|
||||
return self._test_val_step(*args)
|
||||
|
||||
def validation_epoch_end(self, outputs: list):
|
||||
return self._test_val_epoch_end(outputs)
|
||||
|
||||
def test_step(self, *args):
|
||||
return self._test_val_step(*args)
|
||||
|
||||
def test_epoch_end(self, outputs):
|
||||
return self._test_val_epoch_end(outputs, test=True)
|
||||
|
||||
def __init__(self, *params, issubclassed=False):
|
||||
super(CNNRouteGeneratorModel, self).__init__(*params)
|
||||
|
||||
if not issubclassed:
|
||||
# Dataset
|
||||
self.dataset = TrajData(self.hparams.data_param.map_root, mode='generator_all_in_map',
|
||||
length=self.hparams.data_param.dataset_length, normalized=True)
|
||||
self.criterion = nn.MSELoss()
|
||||
|
||||
# Additional Attributes #
|
||||
#######################################################
|
||||
self.map_shape = self.dataset.map_shapes_max
|
||||
self.trajectory_features = 4
|
||||
self.res_net = self.hparams.model_param.use_res_net
|
||||
self.lat_dim = self.hparams.model_param.lat_dim
|
||||
self.feature_dim = self.lat_dim * 10
|
||||
########################################################
|
||||
|
||||
# NN Nodes
|
||||
###################################################
|
||||
#
|
||||
# Utils
|
||||
self.activation = nn.ReLU()
|
||||
self.sigmoid = nn.Sigmoid()
|
||||
|
||||
#
|
||||
# Map Encoder
|
||||
self.enc_conv_0 = ConvModule(self.map_shape, conv_kernel=3, conv_stride=1, conv_padding=1,
|
||||
conv_filters=self.hparams.model_param.filters[0],
|
||||
use_norm=self.hparams.model_param.use_norm,
|
||||
use_bias=self.hparams.model_param.use_bias)
|
||||
|
||||
self.enc_res_1 = ResidualModule(self.enc_conv_0.shape, ConvModule, 2, conv_kernel=5, conv_stride=1,
|
||||
conv_padding=2, conv_filters=self.hparams.model_param.filters[0],
|
||||
use_norm=self.hparams.model_param.use_norm,
|
||||
use_bias=self.hparams.model_param.use_bias)
|
||||
self.enc_conv_1a = ConvModule(self.enc_res_1.shape, conv_kernel=3, conv_stride=1, conv_padding=0,
|
||||
conv_filters=self.hparams.model_param.filters[1],
|
||||
use_norm=self.hparams.model_param.use_norm,
|
||||
use_bias=self.hparams.model_param.use_bias)
|
||||
self.enc_conv_1b = ConvModule(self.enc_conv_1a.shape, conv_kernel=3, conv_stride=2, conv_padding=0,
|
||||
conv_filters=self.hparams.model_param.filters[1],
|
||||
use_norm=self.hparams.model_param.use_norm,
|
||||
use_bias=self.hparams.model_param.use_bias)
|
||||
|
||||
self.enc_res_2 = ResidualModule(self.enc_conv_1b.shape, ConvModule, 2, conv_kernel=5, conv_stride=1,
|
||||
conv_padding=2, conv_filters=self.hparams.model_param.filters[1],
|
||||
use_norm=self.hparams.model_param.use_norm,
|
||||
use_bias=self.hparams.model_param.use_bias)
|
||||
self.enc_conv_2a = ConvModule(self.enc_res_2.shape, conv_kernel=5, conv_stride=1, conv_padding=0,
|
||||
conv_filters=self.hparams.model_param.filters[2],
|
||||
use_norm=self.hparams.model_param.use_norm,
|
||||
use_bias=self.hparams.model_param.use_bias)
|
||||
self.enc_conv_2b = ConvModule(self.enc_conv_2a.shape, conv_kernel=5, conv_stride=1, conv_padding=0,
|
||||
conv_filters=self.hparams.model_param.filters[2],
|
||||
use_norm=self.hparams.model_param.use_norm,
|
||||
use_bias=self.hparams.model_param.use_bias)
|
||||
|
||||
self.enc_res_3 = ResidualModule(self.enc_conv_2b.shape, ConvModule, 2, conv_kernel=7, conv_stride=1,
|
||||
conv_padding=3, conv_filters=self.hparams.model_param.filters[2],
|
||||
use_norm=self.hparams.model_param.use_norm,
|
||||
use_bias=self.hparams.model_param.use_bias)
|
||||
self.enc_conv_3a = ConvModule(self.enc_res_3.shape, conv_kernel=7, conv_stride=1, conv_padding=0,
|
||||
conv_filters=self.hparams.model_param.filters[2],
|
||||
use_norm=self.hparams.model_param.use_norm,
|
||||
use_bias=self.hparams.model_param.use_bias)
|
||||
self.enc_conv_3b = ConvModule(self.enc_conv_3a.shape, conv_kernel=7, conv_stride=1, conv_padding=0,
|
||||
conv_filters=self.hparams.model_param.filters[2],
|
||||
use_norm=self.hparams.model_param.use_norm,
|
||||
use_bias=self.hparams.model_param.use_bias)
|
||||
|
||||
# Trajectory Encoder
|
||||
self.env_gru_1 = nn.GRU(input_size=self.trajectory_features, hidden_size=self.feature_dim,
|
||||
num_layers=3, batch_first=True)
|
||||
|
||||
self.enc_flat = Flatten(self.enc_conv_3b.shape)
|
||||
self.enc_lin_1 = nn.Linear(self.enc_flat.shape, self.feature_dim)
|
||||
|
||||
#
|
||||
# Mixed Encoder
|
||||
self.enc_lin_2 = nn.Linear(self.feature_dim, self.feature_dim)
|
||||
self.enc_norm = nn.BatchNorm1d(self.feature_dim) if self.hparams.model_param.use_norm else lambda x: x
|
||||
|
||||
#
|
||||
# Variational Bottleneck
|
||||
self.mu = nn.Linear(self.feature_dim, self.lat_dim)
|
||||
self.logvar = nn.Linear(self.feature_dim, self.lat_dim)
|
||||
|
||||
#
|
||||
# Alternative Generator
|
||||
self.gen_lin_1 = nn.Linear(self.hparams.model_param.lat_dim, self.feature_dim)
|
||||
|
||||
self.gen_lin_2 = nn.Linear(self.feature_dim, self.enc_flat.shape)
|
||||
|
||||
self.gen_gru_x = nn.GRU(None, None, batch_first=True)
|
||||
|
||||
|
||||
|
||||
def forward(self, batch_x):
|
||||
#
|
||||
# Encode
|
||||
z, mu, logvar = self.encode(batch_x)
|
||||
|
||||
#
|
||||
# Generate
|
||||
alt_tensor = self.generate(z)
|
||||
return alt_tensor, z, mu, logvar
|
||||
|
||||
@staticmethod
|
||||
def reparameterize(mu, logvar):
|
||||
std = torch.exp(0.5 * logvar)
|
||||
eps = torch.randn_like(std)
|
||||
return mu + eps * std
|
||||
|
||||
def encode(self, batch_x):
|
||||
combined_tensor = self.enc_conv_0(batch_x)
|
||||
combined_tensor = self.enc_res_1(combined_tensor) if self.use_res_net else combined_tensor
|
||||
combined_tensor = self.enc_conv_1a(combined_tensor)
|
||||
combined_tensor = self.enc_conv_1b(combined_tensor)
|
||||
combined_tensor = self.enc_res_2(combined_tensor) if self.use_res_net else combined_tensor
|
||||
combined_tensor = self.enc_conv_2a(combined_tensor)
|
||||
combined_tensor = self.enc_conv_2b(combined_tensor)
|
||||
combined_tensor = self.enc_res_3(combined_tensor) if self.use_res_net else combined_tensor
|
||||
combined_tensor = self.enc_conv_3a(combined_tensor)
|
||||
combined_tensor = self.enc_conv_3b(combined_tensor)
|
||||
|
||||
combined_tensor = self.enc_flat(combined_tensor)
|
||||
combined_tensor = self.enc_lin_1(combined_tensor)
|
||||
combined_tensor = self.enc_lin_2(combined_tensor)
|
||||
|
||||
combined_tensor = self.enc_norm(combined_tensor)
|
||||
combined_tensor = self.activation(combined_tensor)
|
||||
combined_tensor = self.enc_lin_2(combined_tensor)
|
||||
combined_tensor = self.enc_norm(combined_tensor)
|
||||
combined_tensor = self.activation(combined_tensor)
|
||||
|
||||
#
|
||||
# Parameter and Sampling
|
||||
mu = self.mu(combined_tensor)
|
||||
logvar = self.logvar(combined_tensor)
|
||||
z = self.reparameterize(mu, logvar)
|
||||
return z, mu, logvar
|
||||
|
||||
def generate(self, z):
|
||||
alt_tensor = self.gen_lin_1(z)
|
||||
alt_tensor = self.activation(alt_tensor)
|
||||
alt_tensor = self.gen_lin_2(alt_tensor)
|
||||
alt_tensor = self.activation(alt_tensor)
|
||||
alt_tensor = self.reshape_to_last_conv(alt_tensor)
|
||||
alt_tensor = self.gen_deconv_1a(alt_tensor)
|
||||
alt_tensor = self.gen_deconv_1b(alt_tensor)
|
||||
alt_tensor = self.gen_deconv_2a(alt_tensor)
|
||||
alt_tensor = self.gen_deconv_2b(alt_tensor)
|
||||
alt_tensor = self.gen_deconv_3a(alt_tensor)
|
||||
alt_tensor = self.gen_deconv_3b(alt_tensor)
|
||||
alt_tensor = self.gen_deconv_out(alt_tensor)
|
||||
# alt_tensor = self.activation(alt_tensor)
|
||||
alt_tensor = self.sigmoid(alt_tensor)
|
||||
return alt_tensor
|
||||
|
||||
def generate_random(self, n=6):
|
||||
maps = [self.map_storage[choice(self.map_storage.keys_list)] for _ in range(n)]
|
||||
|
||||
trajectories = [x.get_random_trajectory() for x in maps]
|
||||
trajectories = [x.draw_in_array(self.map_storage.max_map_size) for x in trajectories]
|
||||
trajectories = [torch.as_tensor(x, dtype=torch.float32) for x in trajectories] * 2
|
||||
trajectories = self._move_to_model_device(torch.stack(trajectories))
|
||||
|
||||
maps = [torch.as_tensor(x.as_array, dtype=torch.float32) for x in maps] * 2
|
||||
maps = self._move_to_model_device(torch.stack(maps))
|
||||
|
||||
labels = self._move_to_model_device(torch.as_tensor([0] * n + [1] * n))
|
||||
return maps, trajectories, labels, self._test_val_step(((maps, trajectories, labels), None), -9999)
|
||||
|
||||
|
||||
class CNNRouteGeneratorDiscriminated(CNNRouteGeneratorModel):
|
||||
|
||||
name = 'CNNRouteGeneratorDiscriminated'
|
||||
|
||||
def training_step(self, batch_xy, batch_nb, *args, **kwargs):
|
||||
batch_x, label = batch_xy
|
||||
|
||||
generated_alternative, z, mu, logvar = self(batch_x)
|
||||
map_array, trajectory = batch_x
|
||||
|
||||
map_stack = torch.cat((map_array, trajectory, generated_alternative), dim=1)
|
||||
pred_label = self.discriminator(map_stack)
|
||||
discriminated_bce_loss = self.criterion(pred_label, label.float().unsqueeze(-1))
|
||||
|
||||
# see Appendix B from VAE paper:
|
||||
# Kingma and Welling. Auto-Encoding Variational Bayes. ICLR, 2014
|
||||
# https://arxiv.org/abs/1312.6114
|
||||
# 0.5 * sum(1 + log(sigma^2) - mu^2 - sigma^2)
|
||||
kld_loss = -0.5 * torch.sum(1 + logvar - mu.pow(2) - logvar.exp())
|
||||
# Dimensional Resizing
|
||||
kld_loss /= reduce(mul, self.in_shape)
|
||||
|
||||
loss = (kld_loss + discriminated_bce_loss) / 2
|
||||
return dict(loss=loss, log=dict(loss=loss,
|
||||
discriminated_bce_loss=discriminated_bce_loss,
|
||||
kld_loss=kld_loss)
|
||||
)
|
||||
|
||||
def _test_val_step(self, batch_xy, batch_nb, *args):
|
||||
batch_x, label = batch_xy
|
||||
|
||||
generated_alternative, z, mu, logvar = self(batch_x)
|
||||
map_array, trajectory = batch_x
|
||||
|
||||
map_stack = torch.cat((map_array, trajectory, generated_alternative), dim=1)
|
||||
pred_label = self.discriminator(map_stack)
|
||||
|
||||
discriminated_bce_loss = self.criterion(pred_label, label.float().unsqueeze(-1))
|
||||
return dict(discriminated_bce_loss=discriminated_bce_loss, batch_nb=batch_nb,
|
||||
pred_label=pred_label, label=label, generated_alternative=generated_alternative)
|
||||
|
||||
def validation_step(self, *args):
|
||||
return self._test_val_step(*args)
|
||||
|
||||
def validation_epoch_end(self, outputs: list):
|
||||
return self._test_val_epoch_end(outputs)
|
||||
|
||||
def _test_val_epoch_end(self, outputs, test=False):
|
||||
evaluation = ROCEvaluation(plot_roc=True)
|
||||
pred_label = torch.cat([x['pred_label'] for x in outputs])
|
||||
labels = torch.cat([x['label'] for x in outputs]).unsqueeze(1)
|
||||
mean_losses = torch.stack([x['discriminated_bce_loss'] for x in outputs]).mean()
|
||||
|
||||
# Sci-py call ROC eval call is eval(true_label, prediction)
|
||||
roc_auc, tpr, fpr = evaluation(labels.cpu().numpy(), pred_label.cpu().numpy(), )
|
||||
if test:
|
||||
# self.logger.log_metrics(score_dict)
|
||||
self.logger.log_image(f'{self.name}_ROC-Curve', plt.gcf(), step=self.global_step)
|
||||
plt.clf()
|
||||
|
||||
maps, trajectories, labels, val_restul_dict = self.generate_random()
|
||||
|
||||
from lib.visualization.generator_eval import GeneratorVisualizer
|
||||
g = GeneratorVisualizer(maps, trajectories, labels, val_restul_dict)
|
||||
fig = g.draw()
|
||||
self.logger.log_image(f'{self.name}_Output', fig, step=self.global_step)
|
||||
plt.clf()
|
||||
|
||||
return dict(mean_losses=mean_losses, roc_auc=roc_auc, epoch=self.current_epoch)
|
||||
|
||||
def test_step(self, *args):
|
||||
return self._test_val_step(*args)
|
||||
|
||||
def test_epoch_end(self, outputs):
|
||||
return self._test_val_epoch_end(outputs, test=True)
|
||||
|
||||
@property
|
||||
def discriminator(self):
|
||||
if self._disc is None:
|
||||
raise RuntimeError('Set the Discriminator first; "set_discriminator(disc_model)')
|
||||
return self._disc
|
||||
|
||||
def set_discriminator(self, disc_model):
|
||||
if self._disc is not None:
|
||||
raise RuntimeError('Discriminator has already been set... What are trying to do?')
|
||||
self._disc = disc_model
|
||||
|
||||
def __init__(self, *params):
|
||||
super(CNNRouteGeneratorDiscriminated, self).__init__(*params, issubclassed=True)
|
||||
|
||||
self._disc = None
|
||||
|
||||
self.criterion = nn.BCELoss()
|
||||
|
||||
self.dataset = TrajData(self.hparams.data_param.map_root, mode='just_route',
|
||||
length=self.hparams.data_param.dataset_length, normalized=True)
|
||||
@ -60,7 +60,7 @@ class ConvHomDetector(LightningBaseModule):
|
||||
super(ConvHomDetector, self).__init__(hparams)
|
||||
|
||||
# Dataset
|
||||
self.dataset = TrajData(self.hparams.data_param.map_root, mode='all_in_map', )
|
||||
self.dataset = TrajData(self.hparams.data_param.map_root, mode='classifier_all_in_map', )
|
||||
|
||||
# Additional Attributes
|
||||
self.map_shape = self.dataset.map_shapes_max
|
||||
|
||||
@ -22,7 +22,7 @@ class Flatten(nn.Module):
|
||||
try:
|
||||
x = torch.randn(self.in_shape).unsqueeze(0)
|
||||
output = self(x)
|
||||
return output.shape[1:]
|
||||
return output.shape[1:] if len(output.shape[1:]) > 1 else output.shape[-1]
|
||||
except Exception as e:
|
||||
print(e)
|
||||
return -1
|
||||
|
||||
@ -1,10 +1,9 @@
|
||||
import shelve
|
||||
from collections import UserDict
|
||||
from pathlib import Path
|
||||
|
||||
import copy
|
||||
from math import sqrt
|
||||
from random import choice
|
||||
from random import Random
|
||||
|
||||
import numpy as np
|
||||
|
||||
@ -53,8 +52,12 @@ class Map(object):
|
||||
assert array_like_map_representation.ndim == 3
|
||||
self.map_array: np.ndarray = array_like_map_representation
|
||||
self.name = name
|
||||
self.prng = Random()
|
||||
pass
|
||||
|
||||
def seed(self, seed):
|
||||
self.prng.seed(seed)
|
||||
|
||||
def __setattr__(self, key, value):
|
||||
super(Map, self).__setattr__(key, value)
|
||||
if key == 'map_array' and self.map_array is not None:
|
||||
@ -102,7 +105,7 @@ class Map(object):
|
||||
return trajectory
|
||||
|
||||
def get_valid_position(self):
|
||||
valid_position = choice(list(self._G.nodes))
|
||||
valid_position = self.prng.choice(list(self._G.nodes))
|
||||
return valid_position
|
||||
|
||||
def get_trajectory_from_vertices(self, *args):
|
||||
|
||||
@ -20,6 +20,8 @@ class Generator:
|
||||
|
||||
self.data_root = Path(data_root)
|
||||
|
||||
|
||||
|
||||
def generate_n_trajectories_m_alternatives(self, n, m, datafile_name, processes=0, **kwargs):
|
||||
datafile_name = datafile_name if datafile_name.endswith('.pik') else f'{str(datafile_name)}.pik'
|
||||
kwargs.update(n=m)
|
||||
|
||||
22
lib/utils/tools.py
Normal file
22
lib/utils/tools.py
Normal file
@ -0,0 +1,22 @@
|
||||
import pickle
|
||||
import shelve
|
||||
from pathlib import Path
|
||||
|
||||
|
||||
def write_to_shelve(file_path, value):
|
||||
check_path(file_path)
|
||||
file_path.parent.mkdir(exist_ok=True, parents=True)
|
||||
with shelve.open(str(file_path), protocol=pickle.HIGHEST_PROTOCOL) as f:
|
||||
new_key = str(len(f))
|
||||
f[new_key] = value
|
||||
|
||||
|
||||
def load_from_shelve(file_path, key):
|
||||
check_path(file_path)
|
||||
with shelve.open(str(file_path)) as d:
|
||||
return d[key]
|
||||
|
||||
|
||||
def check_path(file_path):
|
||||
assert isinstance(file_path, Path)
|
||||
assert str(file_path).endswith('.pik')
|
||||
@ -5,12 +5,13 @@ import lib.variables as V
|
||||
|
||||
class GeneratorVisualizer(object):
|
||||
|
||||
def __init__(self, maps, trajectories, labels, val_result_dict):
|
||||
def __init__(self, **kwargs):
|
||||
# val_results = dict(discriminated_bce_loss, batch_nb, pred_label, label, generated_alternative)
|
||||
self.alternatives = val_result_dict['generated_alternative']
|
||||
self.labels = labels
|
||||
self.trajectories = trajectories
|
||||
self.maps = maps
|
||||
self.alternatives = kwargs.get('generated_alternative')
|
||||
self.labels = kwargs.get('labels')
|
||||
self.trajectories = kwargs.get('trajectories')
|
||||
self.maps = kwargs.get('maps')
|
||||
|
||||
self._map_width, self._map_height = self.maps[0].squeeze().shape
|
||||
self.column_dict_list = self._build_column_dict_list()
|
||||
self._cols = len(self.column_dict_list)
|
||||
@ -24,10 +25,13 @@ class GeneratorVisualizer(object):
|
||||
for idx in range(self.alternatives.shape[0]):
|
||||
image = (self.alternatives[idx]).cpu().numpy().squeeze()
|
||||
label = self.labels[idx].item()
|
||||
# Dirty and Quick hack incomming.
|
||||
if label == V.HOMOTOPIC:
|
||||
hom_alternatives.append(dict(image=image, label='Homotopic'))
|
||||
non_hom_alternatives.append(None)
|
||||
else:
|
||||
non_hom_alternatives.append(dict(image=image, label='NonHomotopic'))
|
||||
hom_alternatives.append(None)
|
||||
for idx in range(max(len(hom_alternatives), len(non_hom_alternatives))):
|
||||
image = (self.maps[idx] + self.trajectories[idx]).cpu().numpy().squeeze()
|
||||
label = 'original'
|
||||
@ -48,10 +52,13 @@ class GeneratorVisualizer(object):
|
||||
|
||||
for idx in range(len(grid.axes_all)):
|
||||
row, col = divmod(idx, len(self.column_dict_list))
|
||||
current_image = self.column_dict_list[col][row]['image']
|
||||
current_label = self.column_dict_list[col][row]['label']
|
||||
grid[idx].imshow(current_image)
|
||||
grid[idx].title.set_text(current_label)
|
||||
if self.column_dict_list[col][row] is not None:
|
||||
current_image = self.column_dict_list[col][row]['image']
|
||||
current_label = self.column_dict_list[col][row]['label']
|
||||
grid[idx].imshow(current_image)
|
||||
grid[idx].title.set_text(current_label)
|
||||
else:
|
||||
continue
|
||||
fig.cbar_mode = 'single'
|
||||
fig.tight_layout()
|
||||
return fig
|
||||
|
||||
6
main.py
6
main.py
@ -37,6 +37,7 @@ main_arg_parser.add_argument("--data_dataset_length", type=int, default=100000,
|
||||
main_arg_parser.add_argument("--data_root", type=str, default='data', help="")
|
||||
main_arg_parser.add_argument("--data_map_root", type=str, default='res/shapes', help="")
|
||||
main_arg_parser.add_argument("--data_normalized", type=strtobool, default=True, help="")
|
||||
main_arg_parser.add_argument("--data_use_preprocessed", type=strtobool, default=True, help="")
|
||||
|
||||
|
||||
# Transformations
|
||||
@ -55,9 +56,10 @@ main_arg_parser.add_argument("--model_type", type=str, default="CNNRouteGenerato
|
||||
main_arg_parser.add_argument("--model_activation", type=str, default="elu", help="")
|
||||
main_arg_parser.add_argument("--model_filters", type=str, default="[16, 32, 64]", help="")
|
||||
main_arg_parser.add_argument("--model_classes", type=int, default=2, help="")
|
||||
main_arg_parser.add_argument("--model_lat_dim", type=int, default=4, help="")
|
||||
main_arg_parser.add_argument("--model_lat_dim", type=int, default=8, help="")
|
||||
main_arg_parser.add_argument("--model_use_bias", type=strtobool, default=True, help="")
|
||||
main_arg_parser.add_argument("--model_use_norm", type=strtobool, default=True, help="")
|
||||
main_arg_parser.add_argument("--model_use_res_net", type=strtobool, default=False, help="")
|
||||
main_arg_parser.add_argument("--model_dropout", type=float, default=0.00, help="")
|
||||
|
||||
# Project
|
||||
@ -115,7 +117,7 @@ def run_lightning_loop(config_obj):
|
||||
# log_save_interval=(model.n_train_batches * 0.2), # TODO: Better Value / Setting
|
||||
checkpoint_callback=checkpoint_callback,
|
||||
logger=logger,
|
||||
val_percent_check=0.05,
|
||||
val_percent_check=0.025,
|
||||
fast_dev_run=config_obj.main.debug,
|
||||
early_stop_callback=None
|
||||
)
|
||||
|
||||
Loading…
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Reference in New Issue
Block a user