diff --git a/.idea/workspace.xml b/.idea/workspace.xml
index ff2b3b5..539e40a 100644
--- a/.idea/workspace.xml
+++ b/.idea/workspace.xml
@@ -2,14 +2,15 @@
<project version="4">
<component name="ChangeListManager">
<list default="true" id="5955480a-c876-43d5-afd7-8717f51f413e" name="Default Changelist" comment="">
- <change afterPath="$PROJECT_DIR$/networks/seperating_adversarial_auto_encoder.py" afterDir="false" />
- <change afterPath="$PROJECT_DIR$/viz/viz_latent.py" afterDir="false" />
<change beforePath="$PROJECT_DIR$/.idea/workspace.xml" beforeDir="false" afterPath="$PROJECT_DIR$/.idea/workspace.xml" afterDir="false" />
+ <change beforePath="$PROJECT_DIR$/dataset.py" beforeDir="false" afterPath="$PROJECT_DIR$/dataset.py" afterDir="false" />
<change beforePath="$PROJECT_DIR$/networks/adverserial_auto_encoder.py" beforeDir="false" afterPath="$PROJECT_DIR$/networks/adverserial_auto_encoder.py" afterDir="false" />
<change beforePath="$PROJECT_DIR$/networks/auto_encoder.py" beforeDir="false" afterPath="$PROJECT_DIR$/networks/auto_encoder.py" afterDir="false" />
<change beforePath="$PROJECT_DIR$/networks/modules.py" beforeDir="false" afterPath="$PROJECT_DIR$/networks/modules.py" afterDir="false" />
+ <change beforePath="$PROJECT_DIR$/networks/seperating_adversarial_auto_encoder.py" beforeDir="false" afterPath="$PROJECT_DIR$/networks/seperating_adversarial_auto_encoder.py" afterDir="false" />
<change beforePath="$PROJECT_DIR$/networks/variational_auto_encoder.py" beforeDir="false" afterPath="$PROJECT_DIR$/networks/variational_auto_encoder.py" afterDir="false" />
<change beforePath="$PROJECT_DIR$/run_models.py" beforeDir="false" afterPath="$PROJECT_DIR$/run_models.py" afterDir="false" />
+ <change beforePath="$PROJECT_DIR$/viz/viz_latent.py" beforeDir="false" afterPath="$PROJECT_DIR$/viz/viz_latent.py" afterDir="false" />
</list>
<option name="EXCLUDED_CONVERTED_TO_IGNORED" value="true" />
<option name="SHOW_DIALOG" value="false" />
@@ -68,7 +69,7 @@
</list>
</option>
</component>
- <component name="RunManager" selected="Python.run_models">
+ <component name="RunManager" selected="Python.viz_latent">
<configuration default="true" type="PythonConfigurationType" factoryName="Python">
<module name="ae_toolbox_torch" />
<option name="INTERPRETER_OPTIONS" value="" />
@@ -95,6 +96,9 @@
<module name="ae_toolbox_torch" />
<option name="INTERPRETER_OPTIONS" value="" />
<option name="PARENT_ENVS" value="true" />
+ <envs>
+ <env name="PYTHONUNBUFFERED" value="1" />
+ </envs>
<option name="SDK_HOME" value="" />
<option name="WORKING_DIRECTORY" value="$PROJECT_DIR$" />
<option name="IS_MODULE_SDK" value="true" />
@@ -129,12 +133,33 @@
<option name="INPUT_FILE" value="" />
<method v="2" />
</configuration>
+ <configuration name="viz_latent" type="PythonConfigurationType" factoryName="Python" temporary="true">
+ <module name="ae_toolbox_torch" />
+ <option name="INTERPRETER_OPTIONS" value="" />
+ <option name="PARENT_ENVS" value="true" />
+ <option name="SDK_HOME" value="" />
+ <option name="WORKING_DIRECTORY" value="$PROJECT_DIR$/viz" />
+ <option name="IS_MODULE_SDK" value="true" />
+ <option name="ADD_CONTENT_ROOTS" value="true" />
+ <option name="ADD_SOURCE_ROOTS" value="true" />
+ <EXTENSION ID="PythonCoverageRunConfigurationExtension" runner="coverage.py" />
+ <option name="SCRIPT_NAME" value="$PROJECT_DIR$/viz/viz_latent.py" />
+ <option name="PARAMETERS" value="" />
+ <option name="SHOW_COMMAND_LINE" value="true" />
+ <option name="EMULATE_TERMINAL" value="false" />
+ <option name="MODULE_MODE" value="false" />
+ <option name="REDIRECT_INPUT" value="false" />
+ <option name="INPUT_FILE" value="" />
+ <method v="2" />
+ </configuration>
<list>
<item itemvalue="Python.run_basic_ae" />
<item itemvalue="Python.run_models" />
+ <item itemvalue="Python.viz_latent" />
</list>
<recent_temporary>
<list>
+ <item itemvalue="Python.viz_latent" />
<item itemvalue="Python.run_models" />
<item itemvalue="Python.run_basic_ae" />
</list>
@@ -153,7 +178,8 @@
<workItem from="1564587420277" duration="6891000" />
<workItem from="1565364574595" duration="1092000" />
<workItem from="1565592214301" duration="53660000" />
- <workItem from="1565793671730" duration="53296000" />
+ <workItem from="1565793671730" duration="53351000" />
+ <workItem from="1566372837067" duration="27899000" />
</task>
<task id="LOCAL-00001" summary="Lightning integration basic ae, dataloaders and dataset">
<created>1565793753423</created>
@@ -183,7 +209,14 @@
<option name="project" value="LOCAL" />
<updated>1566064016196</updated>
</task>
- <option name="localTasksCounter" value="5" />
+ <task id="LOCAL-00005" summary="Done: First VIsualization ToDo: Visualization for all classes, latent space setups">
+ <created>1566366992088</created>
+ <option name="number" value="00005" />
+ <option name="presentableId" value="LOCAL-00005" />
+ <option name="project" value="LOCAL" />
+ <updated>1566366992088</updated>
+ </task>
+ <option name="localTasksCounter" value="6" />
<servers />
</component>
<component name="TypeScriptGeneratedFilesManager">
@@ -205,32 +238,46 @@
<component name="VcsManagerConfiguration">
<MESSAGE value="Lightning integration basic ae, dataloaders and dataset" />
<MESSAGE value="Done: AE, VAE, AAE ToDo: Double AAE, Visualization All Modularized" />
- <option name="LAST_COMMIT_MESSAGE" value="Done: AE, VAE, AAE ToDo: Double AAE, Visualization All Modularized" />
+ <MESSAGE value="Done: First VIsualization ToDo: Visualization for all classes, latent space setups" />
+ <option name="LAST_COMMIT_MESSAGE" value="Done: First VIsualization ToDo: Visualization for all classes, latent space setups" />
</component>
<component name="XDebuggerManager">
<breakpoint-manager>
<breakpoints>
<line-breakpoint enabled="true" suspend="THREAD" type="python-line">
- <url>file://$PROJECT_DIR$/run_models.py</url>
+ <url>file://$PROJECT_DIR$/networks/modules.py</url>
+ <line>206</line>
+ <option name="timeStamp" value="51" />
+ </line-breakpoint>
+ <line-breakpoint enabled="true" suspend="THREAD" type="python-line">
+ <url>file://$PROJECT_DIR$/networks/seperating_adversarial_auto_encoder.py</url>
<line>23</line>
- <option name="timeStamp" value="27" />
+ <option name="timeStamp" value="52" />
+ </line-breakpoint>
+ <line-breakpoint enabled="true" suspend="THREAD" type="python-line">
+ <url>file://$PROJECT_DIR$/viz/viz_latent.py</url>
+ <line>67</line>
+ <option name="timeStamp" value="56" />
</line-breakpoint>
</breakpoints>
<default-breakpoints>
<breakpoint type="python-exception">
- <properties notifyOnTerminate="true" exception="BaseException">
+ <properties notifyOnlyOnFirst="true" notifyOnTerminate="true" ignoreLibraries="true" exception="BaseException">
+ <option name="ignoreLibraries" value="true" />
<option name="notifyOnTerminate" value="true" />
+ <option name="notifyOnlyOnFirst" value="true" />
</properties>
</breakpoint>
</default-breakpoints>
</breakpoint-manager>
</component>
<component name="com.intellij.coverage.CoverageDataManagerImpl">
+ <SUITE FILE_PATH="coverage/ae_toolbox_torch$viz_latent.coverage" NAME="viz_latent Coverage Results" MODIFIED="1566541302103" SOURCE_PROVIDER="com.intellij.coverage.DefaultCoverageFileProvider" RUNNER="coverage.py" COVERAGE_BY_TEST_ENABLED="true" COVERAGE_TRACING_ENABLED="false" WORKING_DIRECTORY="$PROJECT_DIR$/viz" />
<SUITE FILE_PATH="coverage/ae_toolbox_torch$basic_ae_lightning_torch.coverage" NAME="basic_ae_lightning_torch Coverage Results" MODIFIED="1565937164457" 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$basic_ae_lightning.coverage" NAME="basic_ae_lightning Coverage Results" MODIFIED="1565956491159" SOURCE_PROVIDER="com.intellij.coverage.DefaultCoverageFileProvider" RUNNER="coverage.py" COVERAGE_BY_TEST_ENABLED="true" COVERAGE_TRACING_ENABLED="false" WORKING_DIRECTORY="$PROJECT_DIR$" />
<SUITE FILE_PATH="coverage/ae_toolbox_torch$basic_vae_lightning.coverage" NAME="basic_vae_lightning Coverage Results" MODIFIED="1565955311009" 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$run_basic_ae.coverage" NAME="run_basic_ae Coverage Results" MODIFIED="1565966122607" SOURCE_PROVIDER="com.intellij.coverage.DefaultCoverageFileProvider" RUNNER="coverage.py" COVERAGE_BY_TEST_ENABLED="true" COVERAGE_TRACING_ENABLED="false" WORKING_DIRECTORY="$PROJECT_DIR$" />
- <SUITE FILE_PATH="coverage/ae_toolbox_torch$run_models.coverage" NAME="run_models Coverage Results" MODIFIED="1566306843739" 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>
\ No newline at end of file
diff --git a/dataset.py b/dataset.py
index 09763b8..a5c85a1 100644
--- a/dataset.py
+++ b/dataset.py
@@ -163,7 +163,7 @@ class Trajectories(Dataset):
self.data = self.__init_data_(**kwargs)
pass
- def __init_data_(self, **kwargs):
+ def __init_data_(self, **kwargs: dict):
dataDict = dict()
for key, val in kwargs.items():
if key in self.isovistMeasures:
@@ -177,6 +177,7 @@ class Trajectories(Dataset):
return data
def __iter__(self):
+ # FixMe: is that correct?
for i in range(len(self)):
yield self[i]
diff --git a/networks/adverserial_auto_encoder.py b/networks/adverserial_auto_encoder.py
index 996d5b0..53352fe 100644
--- a/networks/adverserial_auto_encoder.py
+++ b/networks/adverserial_auto_encoder.py
@@ -10,7 +10,7 @@ class AdversarialAutoEncoder(AutoEncoder):
def __init__(self, *args, **kwargs):
super(AdversarialAutoEncoder, self).__init__(*args, **kwargs)
- self.discriminator = Discriminator(self.latent_dim, self.dataParams)
+ self.discriminator = Discriminator(self.latent_dim, self.features)
def forward(self, batch):
# Encoder
@@ -18,7 +18,7 @@ class AdversarialAutoEncoder(AutoEncoder):
z = self.encoder(batch)
# Decoder
# First repeat the data accordingly to the batch size
- z_repeatet = Repeater((batch.shape[0], self.dataParams['size'], -1))(z)
+ z_repeatet = Repeater((batch.shape[0], batch.shape[1], -1))(z)
x_hat = self.decoder(z_repeatet)
return z, x_hat
diff --git a/networks/auto_encoder.py b/networks/auto_encoder.py
index cae1214..b72bc59 100644
--- a/networks/auto_encoder.py
+++ b/networks/auto_encoder.py
@@ -7,12 +7,13 @@ from torch import Tensor
# Basic AE-Implementation
class AutoEncoder(AbstractNeuralNetwork, ABC):
- def __init__(self, latent_dim: int, dataParams: dict, **kwargs):
+ def __init__(self, latent_dim: int=0, features: int = 0, **kwargs):
+ assert latent_dim and features
super(AutoEncoder, self).__init__()
- self.dataParams = dataParams
self.latent_dim = latent_dim
+ self.features = features
self.encoder = Encoder(self.latent_dim)
- self.decoder = Decoder(self.latent_dim, self.dataParams['features'])
+ self.decoder = Decoder(self.latent_dim, self.features)
def forward(self, batch: Tensor):
# Encoder
@@ -20,7 +21,7 @@ class AutoEncoder(AbstractNeuralNetwork, ABC):
z = self.encoder(batch)
# Decoder
# First repeat the data accordingly to the batch size
- z_repeatet = Repeater((batch.shape[0], self.dataParams['size'], -1))(z)
+ z_repeatet = Repeater((batch.shape[0], batch.shape[1], -1))(z)
x_hat = self.decoder(z_repeatet)
return z, x_hat
diff --git a/networks/modules.py b/networks/modules.py
index bf73652..0cc5ccf 100644
--- a/networks/modules.py
+++ b/networks/modules.py
@@ -131,13 +131,13 @@ class AvgDimPool(Module):
# Generators, Decoders, Encoders, Discriminators
class Discriminator(Module):
- def __init__(self, latent_dim, dataParams, dropout=.0, activation=ReLU):
+ def __init__(self, latent_dim, features, dropout=.0, activation=ReLU):
super(Discriminator, self).__init__()
- self.dataParams = dataParams
+ self.features = features
self.latent_dim = latent_dim
- self.l1 = Linear(self.latent_dim, self.dataParams['features'] * 10)
- self.l2 = Linear(self.dataParams['features'] * 10, self.dataParams['features'] * 20)
- self.lout = Linear(self.dataParams['features'] * 20, 1)
+ self.l1 = Linear(self.latent_dim, self.features * 10)
+ self.l2 = Linear(self.features * 10, self.features * 20)
+ self.lout = Linear(self.features * 20, 1)
self.dropout = Dropout(dropout)
self.activation = activation()
self.sigmoid = Sigmoid()
diff --git a/networks/seperating_adversarial_auto_encoder.py b/networks/seperating_adversarial_auto_encoder.py
index 858a92f..5bf5fc5 100644
--- a/networks/seperating_adversarial_auto_encoder.py
+++ b/networks/seperating_adversarial_auto_encoder.py
@@ -6,17 +6,17 @@ import torch
class SeperatingAdversarialAutoEncoder(Module):
- def __init__(self, latent_dim, dataParams, **kwargs):
+ 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.dataParams = dataParams
+ self.features = features
self.spatial_encoder = PoolingEncoder(self.latent_dim // 2)
self.temporal_encoder = Encoder(self.latent_dim // 2)
- self.decoder = Decoder(self.latent_dim, self.dataParams['features'])
- self.spatial_discriminator = Discriminator(self.latent_dim // 2, self.dataParams)
- self.temporal_discriminator = Discriminator(self.latent_dim // 2, self.dataParams)
+ 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)
def forward(self, batch):
# Encoder
@@ -25,7 +25,7 @@ class SeperatingAdversarialAutoEncoder(Module):
# Decoder
# First repeat the data accordingly to the batch size
z_concat = torch.cat((z_spatial, z_temporal), dim=-1)
- z_repeatet = Repeater((batch.shape[0], self.dataParams['size'], -1))(z_concat)
+ z_repeatet = Repeater((batch.shape[0], batch.shape[1], -1))(z_concat)
x_hat = self.decoder(z_repeatet)
return z_spatial, z_temporal, x_hat
diff --git a/networks/variational_auto_encoder.py b/networks/variational_auto_encoder.py
index fad33ef..64cb7a9 100644
--- a/networks/variational_auto_encoder.py
+++ b/networks/variational_auto_encoder.py
@@ -10,12 +10,13 @@ class VariationalAutoEncoder(AbstractNeuralNetwork, ABC):
def name(self):
return self.__class__.__name__
- def __init__(self, dataParams, **kwargs):
+ def __init__(self, latent_dim=0, features=0, **kwargs):
+ assert latent_dim and features
super(VariationalAutoEncoder, self).__init__()
- self.dataParams = dataParams
- self.latent_dim = kwargs.get('latent_dim', 2)
+ self.features = features
+ self.latent_dim = latent_dim
self.encoder = Encoder(self.latent_dim, variational=True)
- self.decoder = Decoder(self.latent_dim, self.dataParams['features'], variational=True)
+ self.decoder = Decoder(self.latent_dim, self.features, variational=True)
@staticmethod
def reparameterize(mu, logvar):
@@ -27,7 +28,7 @@ class VariationalAutoEncoder(AbstractNeuralNetwork, ABC):
def forward(self, batch):
mu, logvar = self.encoder(batch)
z = self.reparameterize(mu, logvar)
- repeat = Repeater((batch.shape[0], self.dataParams['size'], -1))
+ repeat = Repeater((batch.shape[0], batch.shape[1], -1))
x_hat = self.decoder(repeat(z))
return x_hat, mu, logvar
diff --git a/run_models.py b/run_models.py
index 5b52e50..6362036 100644
--- a/run_models.py
+++ b/run_models.py
@@ -14,21 +14,35 @@ from torch.nn import BatchNorm1d
from pytorch_lightning import Trainer
from test_tube import Experiment
+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')
+
+
# ToDo: How to implement this better?
# other_classes = [AutoEncoder, AutoEncoderLightningOverrides]
-class Model(VariationalAutoEncoderLightningOverrides, LightningModule):
+class Model(AutoEncoderLightningOverrides, LightningModule):
- def __init__(self, dataParams: dict):
+ 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
super(Model, self).__init__()
- self.dataParams = dataParams
- self.network = VariationalAutoEncoder(self.dataParams)
+ 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.dataParams), shuffle=True, batch_size=100)
+ return DataLoader(DataContainer('data', self.size, self.step), shuffle=True, batch_size=100)
class AdversarialModel(AdversarialAELightningOverrides, LightningModule):
@@ -37,11 +51,15 @@ class AdversarialModel(AdversarialAELightningOverrides, LightningModule):
def name(self):
return self.network.name
- def __init__(self, dataParams: dict):
+ 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
super(AdversarialModel, self).__init__()
- self.dataParams = dataParams
self.normal = Normal(0, 1)
- self.network = AdversarialAutoEncoder(self.dataParams)
+ self.network = AdversarialAutoEncoder(self.latent_dim, self.features)
pass
# This is Fucked up, why do i need to put an additional empty list here?
@@ -52,17 +70,20 @@ class AdversarialModel(AdversarialAELightningOverrides, LightningModule):
@data_loader
def tng_dataloader(self):
- return DataLoader(DataContainer('data', **self.dataParams), shuffle=True, batch_size=100)
+ return DataLoader(DataContainer('data', self.size, self.step), shuffle=True, batch_size=100)
class SeparatingAdversarialModel(SeparatingAdversarialAELightningOverrides, LightningModule):
- def __init__(self, latent_dim, dataParams: dict):
+ 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
super(SeparatingAdversarialModel, self).__init__()
- self.latent_dim = latent_dim
- self.dataParams = dataParams
self.normal = Normal(0, 1)
- self.network = SeperatingAdversarialAutoEncoder(self.latent_dim, self.dataParams)
+ 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?
@@ -78,22 +99,24 @@ class SeparatingAdversarialModel(SeparatingAdversarialAELightningOverrides, Ligh
@data_loader
def tng_dataloader(self):
- return DataLoader(DataContainer('data', **self.dataParams), shuffle=True, batch_size=100)
+ 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
- latent_dim = 4
- model = SeparatingAdversarialModel(latent_dim=latent_dim, dataParams=dict(refresh=False, size=5, step=5,
- features=features, transforms=[BatchNorm1d(features)]
- )
- )
+ tag_dict = dict(features=features, latent_dim=4, size=5, step=6, refresh=False,
+ transforms=[BatchNorm1d(features)])
+ arguments = args.parse_args()
+ arguments.__dict__.update(tag_dict)
+
+ model = SeparatingAdversarialModel(arguments)
# PyTorch summarywriter with a few bells and whistles
outpath = os.path.join(os.getcwd(), 'output', model.name, time.asctime().replace(' ', '_').replace(':', '-'))
os.makedirs(outpath, exist_ok=True)
exp = Experiment(save_dir=outpath)
+ exp.tag(tag_dict=tag_dict)
from pytorch_lightning.callbacks import ModelCheckpoint
@@ -101,9 +124,8 @@ if __name__ == '__main__':
filepath=os.path.join(outpath, 'weights.ckpt'),
save_best_only=True,
verbose=True,
- monitor='val_loss',
+ monitor='tng_loss', # val_loss
mode='min',
-
)
trainer = Trainer(experiment=exp, checkpoint_callback=checkpoint_callback, max_nb_epochs=15) # gpus=[0...LoL]
diff --git a/viz/viz_latent.py b/viz/viz_latent.py
index e4a1b5f..140304c 100644
--- a/viz/viz_latent.py
+++ b/viz/viz_latent.py
@@ -4,6 +4,8 @@ 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
@@ -12,30 +14,28 @@ from sklearn.decomposition import PCA
import seaborn as sns; sns.set()
import matplotlib.pyplot as plt
-from run_models import SeparatingAdversarialModel
-
-path = 'output'
-mylightningmodule = 'weired name, loaded from disk'
-
-
-# FIXME: How to store hyperparamters in testtube element?
-
+from run_models import *
def search_for_weights(folder):
+ while not os.path.exists(folder):
+ if len(os.path.split(folder)) >= 50:
+ raise FileNotFoundError(f'The folder "{folder}" could not be found')
+ folder = os.path.join(os.pardir, folder)
for element in os.scandir(folder):
if os.path.exists(element):
if element.is_dir():
search_for_weights(element.path)
elif element.is_file() and element.name.endswith('.ckpt'):
- load_and_viz(element)
+ load_and_predict(element)
else:
continue
-def load_and_viz(path_like_element):
+def load_and_predict(path_like_element):
# Define Loop to search for models and folder with visualizations
- pretrained_model = SeparatingAdversarialModel.load_from_metrics(
+ model = globals()[path_like_element.path.split(os.sep)[-3]]
+ pretrained_model = model.load_from_metrics(
weights_path=path_like_element.path,
tags_csv=os.path.join(os.path.dirname(path_like_element), 'default', 'version_0', 'meta_tags.csv'),
on_gpu=True if torch.cuda.is_available() else False,
@@ -46,19 +46,26 @@ def load_and_viz(path_like_element):
pretrained_model.eval()
pretrained_model.freeze()
- # Load the data fpr prediction
- dataset = DataContainer('data', 5, 5)
+ # Load the data for prediction
+ dataset = DataContainer(os.path.join(os.pardir, 'data'), 5, 5)
# Do the inference
- predictions = []
- for i in range(len(dataset)):
- z, _ = pretrained_model(dataset[i])
- predictions.append(z)
- predictions = torch.cat(predictions)
- if predictions.shape[-1] <= 1:
+ 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)
+
+
+def viz_latent(prediction):
+ if prediction.shape[-1] <= 1:
raise ValueError('How did this happen?')
- elif predictions.shape[-1] == 2:
- ax = sns.scatterplot(x=predictions[:, 0], y=predictions[:, 1])
+ elif prediction.shape[-1] == 2:
+ ax = sns.scatterplot(x=prediction[:, 0], y=prediction[:, 1])
plt.show()
return ax
else:
@@ -69,3 +76,7 @@ def load_and_viz(path_like_element):
tsne_plot = sns.scatterplot(x=predictions_tsne[:, 0], y=predictions_tsne[:, 1], ax=axs[1])
plt.show()
return fig, axs, pca_plot, tsne_plot
+
+if __name__ == '__main__':
+ path = 'output'
+ search_for_weights(path)
\ No newline at end of file