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Debugging Validation and testing

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Si11ium 2020-03-09 19:18:22 +01:00
parent 4ae333fe5d
commit 6b9696c98e
14 changed files with 28 additions and 116 deletions
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2
.idea/.gitignore generated vendored
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# Default ignored files
/workspace.xml

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.idea/deployment.xml generated
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<?xml version="1.0" encoding="UTF-8"?>
<project version="4">
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<serverData>
<paths name="erlowa@aimachine">
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<mapping deploy="/" local="$PROJECT_DIR$" web="/" />
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.idea/dictionaries/steffen.xml generated
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<component name="ProjectDictionaryState">
<dictionary name="steffen">
<words>
<w>autopad</w>
<w>conv</w>
<w>convolutional</w>
<w>dataloader</w>
<w>dataloaders</w>
<w>datasets</w>
<w>homotopic</w>
<w>hparams</w>
<w>hyperparamter</w>
<w>kingma</w>
<w>logvar</w>
<w>mapname</w>
<w>mapnames</w>
<w>numlayers</w>
<w>reparameterize</w>
<w>softmax</w>
<w>traj</w>
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</dictionary>
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.idea/hom_traj_gen.iml generated
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<?xml version="1.0" encoding="UTF-8"?>
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<content url="file://$MODULE_DIR$" />
<orderEntry type="jdk" jdkName="Remote Python 3.7.6 (sftp://steffen@aimachine:22/home/steffen/envs/traj_gen/bin/python)" jdkType="Python SDK" />
<orderEntry type="sourceFolder" forTests="false" />
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.idea/inspectionProfiles/profiles_settings.xml generated
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.idea/misc.xml generated
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.idea/modules.xml generated
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<?xml version="1.0" encoding="UTF-8"?>
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.idea/vcs.xml generated
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17
lib/models/generators/cnn.py
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@ -1,3 +1,5 @@
from statistics import mean
from random import choice from random import choice
import torch import torch
@ -65,13 +67,12 @@ class CNNRouteGeneratorModel(LightningBaseModule):
def validation_epoch_end(self, outputs): def validation_epoch_end(self, outputs):
evaluation = ROCEvaluation(plot_roc=True) evaluation = ROCEvaluation(plot_roc=True)
predictions = torch.cat([x['prediction'] for x in outputs]) pred_label = torch.cat([x['pred_label'] for x in outputs])
labels = torch.cat([x['label'] for x in outputs]).unsqueeze(1) labels = torch.cat([x['label'] for x in outputs]).unsqueeze(1)
losses = torch.cat([x['discriminated_bce_loss'] for x in outputs]).unsqueeze(1) mean_losses = torch.stack([x['discriminated_bce_loss'] for x in outputs]).mean()
mean_losses = losses.mean()
# Sci-py call ROC eval call is eval(true_label, prediction) # Sci-py call ROC eval call is eval(true_label, prediction)
roc_auc, tpr, fpr = evaluation(labels.cpu().numpy(), predictions.cpu().numpy(), ) roc_auc, tpr, fpr = evaluation(labels.cpu().numpy(), pred_label.cpu().numpy(), )
# self.logger.log_metrics(score_dict) # self.logger.log_metrics(score_dict)
self.logger.log_image(f'{self.name}_ROC-Curve_E{self.current_epoch}', plt.gcf()) self.logger.log_image(f'{self.name}_ROC-Curve_E{self.current_epoch}', plt.gcf())
plt.clf() plt.clf()
@ -103,7 +104,7 @@ class CNNRouteGeneratorModel(LightningBaseModule):
# Dataset # Dataset
self.dataset = TrajData(self.hparams.data_param.map_root, mode='just_route', self.dataset = TrajData(self.hparams.data_param.map_root, mode='just_route',
length=self.hparams.train_param.batch_size * 1000) length=self.hparams.data_param.dataset_length)
# Additional Attributes # Additional Attributes
self.in_shape = self.dataset.map_shapes_max self.in_shape = self.dataset.map_shapes_max
@ -159,6 +160,10 @@ class CNNRouteGeneratorModel(LightningBaseModule):
self.traj_lin = nn.Linear(reduce(mul, self.traj_conv_3.shape), self.feature_dim) self.traj_lin = nn.Linear(reduce(mul, self.traj_conv_3.shape), self.feature_dim)
#
# Mixed Encoder
self.mixed_lin = nn.Linear(self.lat_dim, self.lat_dim)
# #
# Variational Bottleneck # Variational Bottleneck
self.mu = nn.Linear(self.lat_dim, self.hparams.model_param.lat_dim) self.mu = nn.Linear(self.lat_dim, self.hparams.model_param.lat_dim)
@ -242,7 +247,7 @@ class CNNRouteGeneratorModel(LightningBaseModule):
return z, mu, logvar return z, mu, logvar
def generate_random(self, n=6): def generate_random(self, n=6):
maps = [self.map_storage[choice(self.map_storage.keys())] for _ in range(n)] maps = [self.map_storage[choice(self.map_storage.keys)] for _ in range(n)]
trajectories = torch.stack([x.get_random_trajectory() for x in maps] * 2) trajectories = torch.stack([x.get_random_trajectory() for x in maps] * 2)
maps = torch.stack([x.as_2d_array for x in maps] * 2) maps = torch.stack([x.as_2d_array for x in maps] * 2)
labels = torch.as_tensor([0] * n + [1] * n) labels = torch.as_tensor([0] * n + [1] * n)

4
lib/models/homotopy_classification/cnn_based.py
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@ -57,7 +57,8 @@ class ConvHomDetector(LightningBaseModule):
# Model Parameters # Model Parameters
self.in_shape = self.dataset.map_shapes_max self.in_shape = self.dataset.map_shapes_max
assert len(self.in_shape) == 3, f'Image or map shape has to have 3 dims, but had: {len(self.in_shape)}' assert len(self.in_shape) == 3, f'Image or map shape has to have 3 dims, but had: {len(self.in_shape)}'
self.criterion = nn.BCEWithLogitsLoss() self.criterion = nn.BCELoss()
self.sigmoid = nn.Sigmoid()
# NN Nodes # NN Nodes
# ============================ # ============================
@ -100,4 +101,5 @@ class ConvHomDetector(LightningBaseModule):
tensor = self.flatten(tensor) tensor = self.flatten(tensor)
tensor = self.linear(tensor) tensor = self.linear(tensor)
tensor = self.classifier(tensor) tensor = self.classifier(tensor)
tensor = self.sigmoid(tensor)
return tensor return tensor

8
lib/modules/utils.py
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@ -90,7 +90,7 @@ class LightningBaseModule(pl.LightningModule, ABC):
# Data loading # Data loading
# ============================================================================= # =============================================================================
# Map Object # Map Object
self.map_storage = MapStorage(self.hparams.data_param.map_root) self.map_storage = MapStorage(self.hparams.data_param.map_root, load_all=True)
def size(self): def size(self):
return self.shape return self.shape
@ -143,19 +143,19 @@ class LightningBaseModule(pl.LightningModule, ABC):
# Train Dataloader # Train Dataloader
def train_dataloader(self): def train_dataloader(self):
return DataLoader(dataset=self.dataset.train_dataset, shuffle=True, return DataLoader(dataset=self.dataset.train_dataset, shuffle=True,
batch_size=self.hparams.data_param.batchsize, batch_size=self.hparams.train_param.batch_size,
num_workers=self.hparams.data_param.worker) num_workers=self.hparams.data_param.worker)
# Test Dataloader # Test Dataloader
def test_dataloader(self): def test_dataloader(self):
return DataLoader(dataset=self.dataset.test_dataset, shuffle=True, return DataLoader(dataset=self.dataset.test_dataset, shuffle=True,
batch_size=self.hparams.data_param.batchsize, batch_size=self.hparams.train_param.batch_size,
num_workers=self.hparams.data_param.worker) num_workers=self.hparams.data_param.worker)
# Validation Dataloader # Validation Dataloader
def val_dataloader(self): def val_dataloader(self):
return DataLoader(dataset=self.dataset.val_dataset, shuffle=False, return DataLoader(dataset=self.dataset.val_dataset, shuffle=False,
batch_size=self.hparams.data_param.batchsize, batch_size=self.hparams.train_param.batch_size,
num_workers=self.hparams.data_param.worker) num_workers=self.hparams.data_param.worker)

12
lib/objects/map.py
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@ -167,6 +167,10 @@ class Map(object):
class MapStorage(object): class MapStorage(object):
@property
def keys(self):
return list(self.data.keys())
def __init__(self, map_root, load_all=False): def __init__(self, map_root, load_all=False):
self.data = dict() self.data = dict()
self.map_root = Path(map_root) self.map_root = Path(map_root)
@ -175,11 +179,11 @@ class MapStorage(object):
_ = self[map_file.name] _ = self[map_file.name]
def __getitem__(self, item): def __getitem__(self, item):
if item in hasattr(self, item): if item in self.data.keys():
return self.__getattribute__(item) return self.data.get(item)
else: else:
with shelve.open(self.map_root / f'{item}.pik', flag='r') as d: current_map = Map().from_image(self.map_root / item)
self.__setattr__(item, d['map']['map']) self.data.__setitem__(item, np.asarray(current_map))
return self[item] return self[item]

2
main.py
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@ -33,6 +33,7 @@ main_arg_parser.add_argument("--main_seed", type=int, default=69, help="")
# Data Parameters # Data Parameters
main_arg_parser.add_argument("--data_worker", type=int, default=10, help="") main_arg_parser.add_argument("--data_worker", type=int, default=10, help="")
main_arg_parser.add_argument("--data_dataset_length", type=int, default=10000, help="")
main_arg_parser.add_argument("--data_root", type=str, default='data', help="") 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_map_root", type=str, default='res/shapes', help="")
@ -105,6 +106,7 @@ def run_lightning_loop(config_obj):
show_progress_bar=True, show_progress_bar=True,
weights_save_path=logger.log_dir, weights_save_path=logger.log_dir,
gpus=[0] if torch.cuda.is_available() else None, gpus=[0] if torch.cuda.is_available() else None,
check_val_every_n_epoch=1,
# row_log_interval=(model.n_train_batches * 0.1), # TODO: Better Value / Setting # row_log_interval=(model.n_train_batches * 0.1), # TODO: Better Value / Setting
# log_save_interval=(model.n_train_batches * 0.2), # TODO: Better Value / Setting # log_save_interval=(model.n_train_batches * 0.2), # TODO: Better Value / Setting
checkpoint_callback=checkpoint_callback, checkpoint_callback=checkpoint_callback,