Fixed the Model classes, Visualization
This commit is contained in:
Si11ium
parent
0e879bfdb1
commit
7b0b96eaa3
16
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16
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|
||||
<SUITE FILE_PATH="coverage/ae_toolbox_torch$run_basic_ae.coverage" NAME="run_basic_ae Coverage Results" MODIFIED="1565966122607" SOURCE_PROVIDER="com.intellij.coverage.DefaultCoverageFileProvider" RUNNER="coverage.py" COVERAGE_BY_TEST_ENABLED="true" COVERAGE_TRACING_ENABLED="false" WORKING_DIRECTORY="$PROJECT_DIR$" />
|
||||
<SUITE FILE_PATH="coverage/ae_toolbox_torch$run_models.coverage" NAME="run_models Coverage Results" MODIFIED="1566537126647" SOURCE_PROVIDER="com.intellij.coverage.DefaultCoverageFileProvider" RUNNER="coverage.py" COVERAGE_BY_TEST_ENABLED="true" COVERAGE_TRACING_ENABLED="false" WORKING_DIRECTORY="$PROJECT_DIR$" />
|
||||
<SUITE FILE_PATH="coverage/ae_toolbox_torch$dataset.coverage" NAME="dataset Coverage Results" MODIFIED="1565772669750" SOURCE_PROVIDER="com.intellij.coverage.DefaultCoverageFileProvider" RUNNER="coverage.py" COVERAGE_BY_TEST_ENABLED="true" COVERAGE_TRACING_ENABLED="false" WORKING_DIRECTORY="$PROJECT_DIR$/data" />
|
||||
</component>
|
||||
</project>
|
||||
@ -167,7 +167,7 @@ class Trajectories(Dataset):
|
||||
dataDict = dict()
|
||||
for key, val in kwargs.items():
|
||||
if key in self.isovistMeasures:
|
||||
dataDict[key] = torch.tensor(val)
|
||||
dataDict[key] = torch.tensor(val, requires_grad=False)
|
||||
# Check if all keys are of same length
|
||||
assert len(set(x.size()[0] for x in dataDict.values() if torch.is_tensor(x))) <= 1
|
||||
data = torch.stack([dataDict[key] for key in self.isovistMeasures], dim=-1)
|
||||
|
||||
@ -1,7 +1,7 @@
|
||||
from torch.optim import Adam
|
||||
|
||||
from networks.auto_encoder import AutoEncoder
|
||||
from torch.nn.functional import mse_loss
|
||||
from torch.nn import Sequential, Linear, ReLU, Dropout, Sigmoid
|
||||
from torch.distributions import Normal
|
||||
from networks.modules import *
|
||||
import torch
|
||||
|
||||
@ -23,14 +23,10 @@ class AdversarialAutoEncoder(AutoEncoder):
|
||||
return z, x_hat
|
||||
|
||||
|
||||
class AdversarialAELightningOverrides:
|
||||
|
||||
@property
|
||||
def name(self):
|
||||
return self.__class__.__name__
|
||||
|
||||
def forward(self, x):
|
||||
return self.network.forward(x)
|
||||
class AdversarialAELightningOverrides(LightningModuleOverrides):
|
||||
|
||||
def __init__(self):
|
||||
super(AdversarialAELightningOverrides, self).__init__()
|
||||
|
||||
def training_step(self, batch, _, optimizer_i):
|
||||
if optimizer_i == 0:
|
||||
@ -67,5 +63,12 @@ class AdversarialAELightningOverrides:
|
||||
raise RuntimeError('This should not have happened, catch me if u can.')
|
||||
|
||||
|
||||
# This is Fucked up, why do i need to put an additional empty list here?
|
||||
def configure_optimizers(self):
|
||||
return [Adam(self.network.discriminator.parameters(), lr=0.02),
|
||||
Adam([*self.network.encoder.parameters(), *self.network.decoder.parameters()], lr=0.02)],\
|
||||
[]
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
raise PermissionError('Get out of here - never run this module')
|
||||
|
||||
@ -1,3 +1,5 @@
|
||||
from torch.optim import Adam
|
||||
|
||||
from .modules import *
|
||||
from torch.nn.functional import mse_loss
|
||||
from torch import Tensor
|
||||
@ -26,14 +28,10 @@ class AutoEncoder(AbstractNeuralNetwork, ABC):
|
||||
return z, x_hat
|
||||
|
||||
|
||||
class AutoEncoderLightningOverrides:
|
||||
class AutoEncoderLightningOverrides(LightningModuleOverrides):
|
||||
|
||||
@property
|
||||
def name(self):
|
||||
return self.__class__.__name__
|
||||
|
||||
def forward(self, x):
|
||||
return self.network.forward(x)
|
||||
def __init__(self):
|
||||
super(AutoEncoderLightningOverrides, self).__init__()
|
||||
|
||||
def training_step(self, x, batch_nb):
|
||||
# z, x_hat
|
||||
@ -41,6 +39,9 @@ class AutoEncoderLightningOverrides:
|
||||
loss = mse_loss(x, x_hat)
|
||||
return {'loss': loss}
|
||||
|
||||
def configure_optimizers(self):
|
||||
return [Adam(self.parameters(), lr=0.02)]
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
raise PermissionError('Get out of here - never run this module')
|
||||
|
||||
@ -1,11 +1,34 @@
|
||||
import os
|
||||
|
||||
import torch
|
||||
import pytorch_lightning as pl
|
||||
from torch.nn import Module, Linear, ReLU, Tanh, Sigmoid, Dropout, GRU, AvgPool2d
|
||||
from pytorch_lightning import data_loader
|
||||
from torch.nn import Module, Linear, ReLU, Tanh, Sigmoid, Dropout, GRU
|
||||
|
||||
from abc import ABC, abstractmethod
|
||||
|
||||
#######################
|
||||
# Abstract NN Class
|
||||
# Abstract NN Class & Lightning Module
|
||||
from torch.utils.data import DataLoader
|
||||
|
||||
from dataset import DataContainer
|
||||
|
||||
|
||||
class LightningModuleOverrides:
|
||||
|
||||
@property
|
||||
def name(self):
|
||||
return self.__class__.__name__
|
||||
|
||||
def forward(self, x):
|
||||
return self.network.forward(x)
|
||||
|
||||
@data_loader
|
||||
def tng_dataloader(self):
|
||||
num_workers = os.cpu_count() // 2
|
||||
return DataLoader(DataContainer('data', self.size, self.step),
|
||||
shuffle=True, batch_size=100, num_workers=num_workers)
|
||||
|
||||
|
||||
class AbstractNeuralNetwork(Module):
|
||||
|
||||
|
||||
@ -1,3 +1,5 @@
|
||||
from torch.optim import Adam
|
||||
|
||||
from networks.auto_encoder import AutoEncoder
|
||||
from torch.nn.functional import mse_loss
|
||||
from networks.modules import *
|
||||
@ -7,16 +9,15 @@ import torch
|
||||
class SeperatingAdversarialAutoEncoder(Module):
|
||||
|
||||
def __init__(self, latent_dim, features, **kwargs):
|
||||
assert latent_dim % 2 == 0, f'Your latent space needs to be even, not odd, but was: "{latent_dim}"'
|
||||
super(SeperatingAdversarialAutoEncoder, self).__init__()
|
||||
|
||||
self.latent_dim = latent_dim
|
||||
self.features = features
|
||||
self.spatial_encoder = PoolingEncoder(self.latent_dim // 2)
|
||||
self.temporal_encoder = Encoder(self.latent_dim // 2)
|
||||
self.spatial_encoder = PoolingEncoder(self.latent_dim)
|
||||
self.temporal_encoder = Encoder(self.latent_dim)
|
||||
self.decoder = Decoder(self.latent_dim, self.features)
|
||||
self.spatial_discriminator = Discriminator(self.latent_dim // 2, self.features)
|
||||
self.temporal_discriminator = Discriminator(self.latent_dim // 2, self.features)
|
||||
self.spatial_discriminator = Discriminator(self.latent_dim, self.features)
|
||||
self.temporal_discriminator = Discriminator(self.latent_dim, self.features)
|
||||
|
||||
def forward(self, batch):
|
||||
# Encoder
|
||||
@ -30,14 +31,10 @@ class SeperatingAdversarialAutoEncoder(Module):
|
||||
return z_spatial, z_temporal, x_hat
|
||||
|
||||
|
||||
class SeparatingAdversarialAELightningOverrides:
|
||||
class SeparatingAdversarialAELightningOverrides(LightningModuleOverrides):
|
||||
|
||||
@property
|
||||
def name(self):
|
||||
return self.__class__.__name__
|
||||
|
||||
def forward(self, x):
|
||||
return self.network.forward(x)
|
||||
def __init__(self):
|
||||
super(SeparatingAdversarialAELightningOverrides, self).__init__()
|
||||
|
||||
def training_step(self, batch, _, optimizer_i):
|
||||
spatial_latent_fake, temporal_latent_fake, batch_hat = self.network.forward(batch)
|
||||
@ -91,6 +88,17 @@ class SeparatingAdversarialAELightningOverrides:
|
||||
else:
|
||||
raise RuntimeError('This should not have happened, catch me if u can.')
|
||||
|
||||
# This is Fucked up, why do i need to put an additional empty list here?
|
||||
def configure_optimizers(self):
|
||||
return [Adam([*self.network.spatial_discriminator.parameters(), *self.network.spatial_encoder.parameters()]
|
||||
, lr=0.02),
|
||||
Adam([*self.network.temporal_discriminator.parameters(), *self.network.temporal_encoder.parameters()]
|
||||
, lr=0.02),
|
||||
Adam([*self.network.temporal_encoder.parameters(),
|
||||
*self.network.spatial_encoder.parameters(),
|
||||
*self.network.decoder.parameters()]
|
||||
, lr=0.02)], []
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
raise PermissionError('Get out of here - never run this module')
|
||||
|
||||
@ -1,3 +1,5 @@
|
||||
from torch.optim import Adam
|
||||
|
||||
from .modules import *
|
||||
from torch.nn.functional import mse_loss
|
||||
|
||||
@ -33,14 +35,10 @@ class VariationalAutoEncoder(AbstractNeuralNetwork, ABC):
|
||||
return x_hat, mu, logvar
|
||||
|
||||
|
||||
class VariationalAutoEncoderLightningOverrides:
|
||||
class VariationalAutoEncoderLightningOverrides(LightningModuleOverrides):
|
||||
|
||||
@property
|
||||
def name(self):
|
||||
return self.network.name
|
||||
|
||||
def forward(self, x):
|
||||
return self.network.forward(x)
|
||||
def __init__(self):
|
||||
super(VariationalAutoEncoderLightningOverrides, self).__init__()
|
||||
|
||||
def training_step(self, x, _):
|
||||
x_hat, logvar, mu = self.forward(x)
|
||||
@ -53,6 +51,9 @@ class VariationalAutoEncoderLightningOverrides:
|
||||
KLD = -0.5 * torch.sum(1 + logvar - mu.pow(2) - logvar.exp())
|
||||
return {'loss': BCE + KLD}
|
||||
|
||||
def configure_optimizers(self):
|
||||
return [Adam(self.parameters(), lr=0.02)]
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
raise PermissionError('Get out of here - never run this module')
|
||||
|
||||
@ -1,3 +1,5 @@
|
||||
from torch.distributions import Normal
|
||||
|
||||
from networks.auto_encoder import *
|
||||
import os
|
||||
import time
|
||||
@ -18,90 +20,54 @@ from argparse import Namespace
|
||||
from argparse import ArgumentParser
|
||||
|
||||
args = ArgumentParser()
|
||||
args.add_argument('step')
|
||||
args.add_argument('features')
|
||||
args.add_argument('size')
|
||||
args.add_argument('latent_dim')
|
||||
args.add_argument('--step', default=0)
|
||||
args.add_argument('--features', default=0)
|
||||
args.add_argument('--size', default=0)
|
||||
args.add_argument('--latent_dim', default=0)
|
||||
args.add_argument('--model', default='Model')
|
||||
|
||||
|
||||
# ToDo: How to implement this better?
|
||||
# other_classes = [AutoEncoder, AutoEncoderLightningOverrides]
|
||||
class Model(AutoEncoderLightningOverrides, LightningModule):
|
||||
|
||||
def __init__(self, latent_dim=0, size=0, step=0, features=0, **kwargs):
|
||||
assert all([x in args for x in ['step', 'size', 'latent_dim', 'features']])
|
||||
self.size = args.size
|
||||
self.latent_dim = args.latent_dim
|
||||
self.features = args.features
|
||||
self.step = args.step
|
||||
def __init__(self, parameters, **kwargs):
|
||||
assert all([x in parameters for x in ['step', 'size', 'latent_dim', 'features']])
|
||||
self.size = parameters.size
|
||||
self.latent_dim = parameters.latent_dim
|
||||
self.features = parameters.features
|
||||
self.step = parameters.step
|
||||
super(Model, self).__init__()
|
||||
self.network = AutoEncoder(self.latent_dim, self.features)
|
||||
|
||||
def configure_optimizers(self):
|
||||
return [Adam(self.parameters(), lr=0.02)]
|
||||
|
||||
@data_loader
|
||||
def tng_dataloader(self):
|
||||
return DataLoader(DataContainer('data', self.size, self.step), shuffle=True, batch_size=100)
|
||||
|
||||
|
||||
class AdversarialModel(AdversarialAELightningOverrides, LightningModule):
|
||||
|
||||
@property
|
||||
def name(self):
|
||||
return self.network.name
|
||||
|
||||
def __init__(self, args: Namespace, **kwargs):
|
||||
assert all([x in args for x in ['step', 'size', 'latent_dim', 'features']])
|
||||
self.size = args.size
|
||||
self.latent_dim = args.latent_dim
|
||||
self.features = args.features
|
||||
self.step = args.step
|
||||
def __init__(self, parameters: Namespace, **kwargs):
|
||||
assert all([x in parameters for x in ['step', 'size', 'latent_dim', 'features']])
|
||||
self.size = parameters.size
|
||||
self.latent_dim = parameters.latent_dim
|
||||
self.features = parameters.features
|
||||
self.step = parameters.step
|
||||
super(AdversarialModel, self).__init__()
|
||||
self.normal = Normal(0, 1)
|
||||
self.network = AdversarialAutoEncoder(self.latent_dim, self.features)
|
||||
pass
|
||||
|
||||
# This is Fucked up, why do i need to put an additional empty list here?
|
||||
def configure_optimizers(self):
|
||||
return [Adam(self.network.discriminator.parameters(), lr=0.02),
|
||||
Adam([*self.network.encoder.parameters(), *self.network.decoder.parameters()], lr=0.02)],\
|
||||
[]
|
||||
|
||||
@data_loader
|
||||
def tng_dataloader(self):
|
||||
return DataLoader(DataContainer('data', self.size, self.step), shuffle=True, batch_size=100)
|
||||
|
||||
|
||||
class SeparatingAdversarialModel(SeparatingAdversarialAELightningOverrides, LightningModule):
|
||||
|
||||
def __init__(self, args: Namespace, **kwargs):
|
||||
assert all([x in args for x in ['step', 'size', 'latent_dim', 'features']])
|
||||
self.size = args.size
|
||||
self.latent_dim = args.latent_dim
|
||||
self.features = args.features
|
||||
self.step = args.step
|
||||
def __init__(self, parameters: Namespace, **kwargs):
|
||||
assert all([x in parameters for x in ['step', 'size', 'latent_dim', 'features']])
|
||||
self.size = parameters.size
|
||||
self.latent_dim = parameters.latent_dim
|
||||
self.features = parameters.features
|
||||
self.step = parameters.step
|
||||
super(SeparatingAdversarialModel, self).__init__()
|
||||
self.normal = Normal(0, 1)
|
||||
self.network = SeperatingAdversarialAutoEncoder(self.latent_dim, self.features, **kwargs)
|
||||
pass
|
||||
|
||||
# This is Fucked up, why do i need to put an additional empty list here?
|
||||
def configure_optimizers(self):
|
||||
return [Adam([*self.network.spatial_discriminator.parameters(), *self.network.spatial_encoder.parameters()]
|
||||
, lr=0.02),
|
||||
Adam([*self.network.temporal_discriminator.parameters(), *self.network.temporal_encoder.parameters()]
|
||||
, lr=0.02),
|
||||
Adam([*self.network.temporal_encoder.parameters(),
|
||||
*self.network.spatial_encoder.parameters(),
|
||||
*self.network.decoder.parameters()]
|
||||
, lr=0.02)], []
|
||||
|
||||
@data_loader
|
||||
def tng_dataloader(self):
|
||||
num_workers = os.cpu_count() // 2
|
||||
return DataLoader(DataContainer('data', self.size, self.step), shuffle=True, batch_size=100, num_workers=num_workers)
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
features = 6
|
||||
@ -110,7 +76,7 @@ if __name__ == '__main__':
|
||||
arguments = args.parse_args()
|
||||
arguments.__dict__.update(tag_dict)
|
||||
|
||||
model = SeparatingAdversarialModel(arguments)
|
||||
model = globals()[arguments.model](arguments)
|
||||
|
||||
# PyTorch summarywriter with a few bells and whistles
|
||||
outpath = os.path.join(os.getcwd(), 'output', model.name, time.asctime().replace(' ', '_').replace(':', '-'))
|
||||
@ -124,7 +90,7 @@ if __name__ == '__main__':
|
||||
filepath=os.path.join(outpath, 'weights.ckpt'),
|
||||
save_best_only=True,
|
||||
verbose=True,
|
||||
monitor='tng_loss', # val_loss
|
||||
monitor='val_loss', # val_loss
|
||||
mode='min',
|
||||
)
|
||||
|
||||
|
||||
@ -1,21 +1,17 @@
|
||||
# TODO: THIS
|
||||
import seaborn as sb
|
||||
import torch
|
||||
from torch.utils.data import DataLoader
|
||||
from pytorch_lightning import data_loader
|
||||
from dataset import DataContainer
|
||||
from collections import defaultdict
|
||||
from tqdm import tqdm
|
||||
import os
|
||||
|
||||
from sklearn.manifold import TSNE
|
||||
from sklearn.decomposition import PCA
|
||||
|
||||
import seaborn as sns; sns.set()
|
||||
import seaborn as sns
|
||||
import matplotlib.pyplot as plt
|
||||
|
||||
from run_models import *
|
||||
|
||||
sns.set()
|
||||
|
||||
|
||||
def search_for_weights(folder):
|
||||
while not os.path.exists(folder):
|
||||
if len(os.path.split(folder)) >= 50:
|
||||
@ -32,6 +28,8 @@ def search_for_weights(folder):
|
||||
|
||||
|
||||
def load_and_predict(path_like_element):
|
||||
if any([x.name.endswith('.png') for x in os.scandir(os.path.dirname(path_like_element))]):
|
||||
return
|
||||
|
||||
# Define Loop to search for models and folder with visualizations
|
||||
model = globals()[path_like_element.path.split(os.sep)[-3]]
|
||||
@ -46,36 +44,50 @@ def load_and_predict(path_like_element):
|
||||
pretrained_model.eval()
|
||||
pretrained_model.freeze()
|
||||
|
||||
# Load the data for prediction
|
||||
dataset = DataContainer(os.path.join(os.pardir, 'data'), 5, 5)
|
||||
with torch.no_grad():
|
||||
|
||||
# Do the inference
|
||||
prediction_dict = defaultdict(list)
|
||||
for i in tqdm(range(len(dataset)), total=len(dataset)):
|
||||
p_X = pretrained_model(dataset[i].unsqueeze(0))
|
||||
for idx in range(len(p_X) - 1):
|
||||
prediction_dict[idx].append(p_X[idx])
|
||||
# Load the data for prediction
|
||||
dataset = DataContainer(os.path.join(os.pardir, 'data'), 5, 5)
|
||||
|
||||
# Do the inference
|
||||
prediction_dict = defaultdict(list)
|
||||
for i in tqdm(range(len(dataset)), total=len(dataset)):
|
||||
p_X = pretrained_model(dataset[i].unsqueeze(0))
|
||||
for idx in range(len(p_X) - 1):
|
||||
prediction_dict[idx].append(p_X[idx])
|
||||
|
||||
predictions = [torch.cat(prediction).detach().numpy() for prediction in prediction_dict.values()]
|
||||
for prediction in predictions:
|
||||
viz_latent(prediction)
|
||||
for idx, prediction in enumerate(predictions):
|
||||
plot, _ = viz_latent(prediction)
|
||||
plot.savefig(os.path.join(os.path.dirname(path_like_element), f'latent_space_{idx}.png'))
|
||||
|
||||
|
||||
def viz_latent(prediction):
|
||||
def viz_latent(prediction, title=f'Latent Space '):
|
||||
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])
|
||||
plt.show()
|
||||
return ax
|
||||
try:
|
||||
plt.show()
|
||||
except:
|
||||
pass
|
||||
return ax.figure, (ax)
|
||||
else:
|
||||
fig, axs = plt.subplots(ncols=2)
|
||||
predictions_pca = PCA(n_components=2)
|
||||
predictions_tsne = TSNE(n_components=2)
|
||||
pca_plot = sns.scatterplot(x=predictions_pca[:, 0], y=predictions_pca[:, 1], ax=axs[0])
|
||||
tsne_plot = sns.scatterplot(x=predictions_tsne[:, 0], y=predictions_tsne[:, 1], ax=axs[1])
|
||||
plt.show()
|
||||
return fig, axs, pca_plot, tsne_plot
|
||||
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, )
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
path = 'output'
|
||||
|
||||
Loading…
x
Reference in New Issue
Block a user