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Si11ium 2020-03-11 17:10:19 +01:00
parent 1b5a7dc69e
commit 1f4edae95c
12 changed files with 157 additions and 93 deletions
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15
datasets/trajectory_dataset.py
View File

@ -18,10 +18,12 @@ class TrajDataset(Dataset):
def map_shape(self):
return self.map.as_array.shape
def __init__(self, *args, maps_root: Union[Path, str] = '', mapname='tate_sw',
length=100000, mode='separated_arrays', embedding_size=None, preserve_equal_samples=False, **kwargs):
def __init__(self, *args, maps_root: Union[Path, str] = '', mapname='tate_sw', normalized=True,
length=100000, mode='separated_arrays', embedding_size=None, preserve_equal_samples=False,
**kwargs):
super(TrajDataset, self).__init__()
assert mode.lower() in ['vectors', 'all_in_map', 'separated_arrays', 'just_route']
self.normalized = normalized
self.preserve_equal_samples = preserve_equal_samples
self.mode = mode
self.mapname = mapname if mapname.endswith('.bmp') else f'{mapname}.bmp'
@ -58,6 +60,10 @@ class TrajDataset(Dataset):
trajectory = trajectory.draw_in_array(self.map_shape)
alternative = alternative.draw_in_array(self.map_shape)
if self.mode == 'separated_arrays':
if self.normalized:
map_array = map_array / V.WHITE
trajectory = trajectory / V.WHITE
alternative = alternative / V.WHITE
return (map_array, trajectory, label), alternative
else:
return np.concatenate((map_array, trajectory, alternative)), label
@ -86,8 +92,9 @@ class TrajData(object):
def name(self):
return self.__class__.__name__
def __init__(self, map_root, length=100000, mode='separated_arrays', **_):
def __init__(self, map_root, length=100000, mode='separated_arrays', normalized=True, **_):
self.normalized = normalized
self.mode = mode
self.maps_root = Path(map_root)
self.length = length
@ -100,7 +107,7 @@ class TrajData(object):
# find max image size among available maps:
max_map_size = (1, ) + tuple(reversed(tuple(map(max, *[Image.open(map_file).size for map_file in map_files]))))
return ConcatDataset([TrajDataset(maps_root=self.maps_root, mapname=map_file.name, length=equal_split,
mode=self.mode, embedding_size=max_map_size,
mode=self.mode, embedding_size=max_map_size, normalized=self.normalized,
preserve_equal_samples=True)
for map_file in map_files])

111
lib/models/generators/cnn.py
View File

@ -27,20 +27,21 @@ class CNNRouteGeneratorModel(LightningBaseModule):
def training_step(self, batch_xy, batch_nb, *args, **kwargs):
batch_x, alternative = batch_xy
generated_alternative, z, mu, logvar = self(batch_x)
mse_loss = self.criterion(generated_alternative, alternative)
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 /= reduce(mul, self.in_shape)
loss = (kld_loss + mse_loss) / 2
return dict(loss=loss, log=dict(loss=loss, mse_loss=mse_loss, kld_loss=kld_loss))
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
batch_x, _ = batch_xy
map_array, trajectory, label = batch_x
generated_alternative, z, mu, logvar = self(batch_x)
@ -48,18 +49,12 @@ class CNNRouteGeneratorModel(LightningBaseModule):
return dict(batch_nb=batch_nb, label=label, generated_alternative=generated_alternative, pred_label=-1)
def _test_val_epoch_end(self, outputs, test=False):
labels = torch.cat([x['label'] for x in outputs]).unsqueeze(1)
if test:
self.logger.log_image(f'{self.name}_ROC-Curve', plt.gcf())
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)
self.logger.log_image(f'{self.name}_Output', fig, step=self.global_step)
return dict(epoch=self.current_epoch)
@ -81,69 +76,88 @@ class CNNRouteGeneratorModel(LightningBaseModule):
if not issubclassed:
# Dataset
self.dataset = TrajData(self.hparams.data_param.map_root, mode='separated_arrays',
length=self.hparams.data_param.dataset_length)
length=self.hparams.data_param.dataset_length, normalized=True)
self.criterion = nn.MSELoss()
# Additional Attributes
self.in_shape = self.dataset.map_shapes_max
# Todo: Better naming and size in Parameters
self.feature_dim = 10
self.lat_dim = self.feature_dim + self.feature_dim + 1
self.feature_dim = self.hparams.model_param.lat_dim * 10
self.feature_mixed_dim = self.feature_dim + self.feature_dim + 1
# NN Nodes
###################################################
#
# Utils
self.relu = nn.ReLU()
self.activation = nn.ReLU()
self.sigmoid = nn.Sigmoid()
#
# Map Encoder
self.map_conv_0 = ConvModule(self.in_shape, conv_kernel=3, conv_stride=1, conv_padding=1,
conv_filters=self.hparams.model_param.filters[0])
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.map_res_1 = ResidualModule(self.map_conv_0.shape, ConvModule, 2, conv_kernel=3, conv_stride=1,
conv_padding=1, conv_filters=self.hparams.model_param.filters[0])
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.map_conv_1 = ConvModule(self.map_res_1.shape, conv_kernel=3, conv_stride=1, conv_padding=0,
conv_filters=self.hparams.model_param.filters[1])
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.map_res_2 = ResidualModule(self.map_conv_1.shape, ConvModule, 2, conv_kernel=3, conv_stride=1,
conv_padding=1, conv_filters=self.hparams.model_param.filters[1])
conv_padding=1, 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.map_conv_2 = ConvModule(self.map_res_2.shape, conv_kernel=3, conv_stride=1, conv_padding=0,
conv_filters=self.hparams.model_param.filters[2])
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.map_res_3 = ResidualModule(self.map_conv_2.shape, ConvModule, 2, conv_kernel=3, conv_stride=1,
conv_padding=1, conv_filters=self.hparams.model_param.filters[2])
conv_padding=1, 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.map_conv_3 = ConvModule(self.map_res_3.shape, conv_kernel=5, conv_stride=1, conv_padding=0,
conv_filters=self.hparams.model_param.filters[2]*2)
conv_filters=self.hparams.model_param.filters[2]*2,
use_norm=self.hparams.model_param.use_norm,
use_bias=self.hparams.model_param.use_bias)
self.map_flat = Flatten(self.map_conv_3.shape)
self.map_lin = nn.Linear(reduce(mul, self.map_conv_3.shape), self.feature_dim)
#
# Trajectory Encoder
self.traj_conv_1 = ConvModule(self.in_shape, conv_kernel=3, conv_stride=1, conv_padding=0,
conv_filters=self.hparams.model_param.filters[0])
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.traj_conv_2 = ConvModule(self.traj_conv_1.shape, conv_kernel=3, conv_stride=1, conv_padding=0,
conv_filters=self.hparams.model_param.filters[0])
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.traj_conv_3 = ConvModule(self.traj_conv_2.shape, conv_kernel=3, conv_stride=1, conv_padding=0,
conv_filters=self.hparams.model_param.filters[0])
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.traj_flat = Flatten(self.traj_conv_3.shape)
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)
self.mixed_lin = nn.Linear(self.feature_mixed_dim, self.feature_mixed_dim)
self.mixed_norm = nn.BatchNorm1d(self.feature_mixed_dim) if self.hparams.model_param.use_norm else lambda x: x
#
# Variational Bottleneck
self.mu = nn.Linear(self.lat_dim, self.hparams.model_param.lat_dim)
self.logvar = nn.Linear(self.lat_dim, self.hparams.model_param.lat_dim)
self.mu = nn.Linear(self.feature_mixed_dim, self.hparams.model_param.lat_dim)
self.logvar = nn.Linear(self.feature_mixed_dim, self.hparams.model_param.lat_dim)
#
# Alternative Generator
@ -153,13 +167,17 @@ class CNNRouteGeneratorModel(LightningBaseModule):
self.reshape_to_map = Flatten(reduce(mul, self.traj_conv_3.shape), self.traj_conv_3.shape)
self.alt_deconv_1 = DeConvModule(self.traj_conv_3.shape, self.hparams.model_param.filters[2],
conv_padding=0, conv_kernel=5, conv_stride=1)
conv_padding=0, conv_kernel=5, 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=3, conv_stride=1)
conv_padding=0, conv_kernel=3, 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=3, conv_stride=1)
conv_padding=1, conv_kernel=3, 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)
conv_padding=1, conv_kernel=3, conv_stride=1,
use_norm=self.hparams.model_param.use_norm)
def forward(self, batch_x):
#
@ -173,7 +191,6 @@ class CNNRouteGeneratorModel(LightningBaseModule):
#
# Generate
alt_tensor = self.generate(z)
return alt_tensor, z, mu, logvar
@staticmethod
@ -184,13 +201,16 @@ class CNNRouteGeneratorModel(LightningBaseModule):
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.sigmoid(alt_tensor)
# alt_tensor = self.activation(alt_tensor)
alt_tensor = self.sigmoid(alt_tensor)
return alt_tensor
def encode(self, map_array, trajectory, label):
@ -211,23 +231,26 @@ class CNNRouteGeneratorModel(LightningBaseModule):
traj_tensor = self.traj_lin(traj_tensor)
mixed_tensor = torch.cat((map_tensor, traj_tensor, label.float().unsqueeze(-1)), dim=1)
mixed_tensor = self.relu(mixed_tensor)
mixed_tensor = self.mixed_norm(mixed_tensor)
mixed_tensor = self.activation(mixed_tensor)
mixed_tensor = self.mixed_lin(mixed_tensor)
mixed_tensor = self.relu(mixed_tensor)
mixed_tensor = self.mixed_norm(mixed_tensor)
mixed_tensor = self.activation(mixed_tensor)
#
# Parameter and Sampling
mu = self.mu(mixed_tensor)
logvar = self.logvar(mixed_tensor)
# logvar = torch.clamp(logvar, min=0, max=10)
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)]
trajectories = [x.get_random_trajectory() for x in maps] * 2
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]
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
@ -294,14 +317,14 @@ class CNNRouteGeneratorDiscriminated(CNNRouteGeneratorModel):
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())
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)
self.logger.log_image(f'{self.name}_Output', fig, step=self.global_step)
return dict(mean_losses=mean_losses, roc_auc=roc_auc, epoch=self.current_epoch)
@ -330,4 +353,4 @@ class CNNRouteGeneratorDiscriminated(CNNRouteGeneratorModel):
self.criterion = nn.BCELoss()
self.dataset = TrajData(self.hparams.data_param.map_root, mode='just_route',
length=self.hparams.data_param.dataset_length)
length=self.hparams.data_param.dataset_length, normalized=True)

6
lib/modules/blocks.py
View File

@ -4,11 +4,11 @@ import torch
from torch import nn
from lib.modules.utils import AutoPad, Interpolate
#
# Sub - Modules
###################
class ConvModule(nn.Module):
@property
@ -60,7 +60,7 @@ class DeConvModule(nn.Module):
def __init__(self, in_shape, conv_filters=3, conv_kernel=5, conv_stride=1, conv_padding=0,
dropout: Union[int, float] = 0, autopad=False,
activation: Union[None, nn.Module] = nn.ReLU, interpolation_scale=None,
use_bias=True, normalize=False):
use_bias=True, use_norm=False):
super(DeConvModule, self).__init__()
in_channels, height, width = in_shape[0], in_shape[1], in_shape[2]
self.padding = conv_padding
@ -70,7 +70,7 @@ class DeConvModule(nn.Module):
self.autopad = AutoPad() if autopad else lambda x: x
self.interpolation = Interpolate(scale_factor=interpolation_scale) if interpolation_scale else lambda x: x
self.norm = nn.BatchNorm2d(in_channels, eps=1e-04, affine=False) if normalize else lambda x: x
self.norm = nn.BatchNorm2d(in_channels, eps=1e-04, affine=False) if use_norm else lambda x: x
self.dropout = nn.Dropout2d(dropout) if dropout else lambda x: x
self.de_conv = nn.ConvTranspose2d(in_channels, self.conv_filters, conv_kernel, bias=use_bias,
padding=self.padding, stride=self.stride)

2
lib/modules/model_parts.py
View File

@ -24,7 +24,7 @@ class Generator(nn.Module):
self.lat_dim = lat_dim
self.dropout = dropout
self.l1 = nn.Linear(self.lat_dim, reduce(mul, re_shape), bias=use_bias)
# re_shape = (self.lat_dim // reduce(mul, re_shape[1:]), ) + tuple(re_shape[1:])
# re_shape = (self.feature_mixed_dim // reduce(mul, re_shape[1:]), ) + tuple(re_shape[1:])
self.flat = Flatten(to=re_shape)

29
lib/modules/utils.py
View File

@ -67,6 +67,23 @@ class AutoPad(nn.Module):
return x
class WeightInit:
def __init__(self, in_place_init_function):
self.in_place_init_function = in_place_init_function
def __call__(self, m):
if hasattr(m, 'weight'):
if isinstance(m.weight, torch.Tensor):
if m.weight.ndim < 2:
m.weight.data.fill_(0.01)
else:
self.in_place_init_function(m.weight)
if hasattr(m, 'bias'):
if isinstance(m.bias, torch.Tensor):
m.bias.data.fill_(0.01)
class LightningBaseModule(pl.LightningModule, ABC):
@classmethod
@ -128,15 +145,9 @@ class LightningBaseModule(pl.LightningModule, ABC):
def test_epoch_end(self, outputs):
raise NotImplementedError
def init_weights(self):
def _weight_init(m):
if hasattr(m, 'weight'):
if isinstance(m.weight, torch.Tensor):
torch.nn.init.xavier_uniform_(m.weight)
if hasattr(m, 'bias'):
if isinstance(m.bias, torch.Tensor):
m.bias.data.fill_(0.01)
self.apply(_weight_init)
def init_weights(self, in_place_init_func_=nn.init.xavier_uniform_):
weight_initializer = WeightInit(in_place_init_function=in_place_init_func_)
self.apply(weight_initializer)
# Dataloaders
# ================================================================================

1
lib/utils/config.py
View File

@ -8,6 +8,7 @@ from pathlib import Path
from lib.models.generators.cnn import CNNRouteGeneratorModel, CNNRouteGeneratorDiscriminated
from lib.models.homotopy_classification.cnn_based import ConvHomDetector
from lib.utils.model_io import ModelParameters
from lib.utils.transforms import AsArray
def is_jsonable(x):

8
lib/utils/logging.py
View File

@ -5,10 +5,12 @@ from pytorch_lightning.loggers.neptune import NeptuneLogger
from pytorch_lightning.loggers.test_tube import TestTubeLogger
from lib.utils.config import Config
import numpy as np
class Logger(LightningLoggerBase):
media_dir = 'media'
@property
def experiment(self):
if self.debug:
@ -84,7 +86,9 @@ class Logger(LightningLoggerBase):
def log_image(self, name, image, **kwargs):
self.neptunelogger.log_image(name, image, **kwargs)
image.savefig(self.log_dir / name)
step = kwargs.get('step', None)
name = f'{step}_{name}' if step is not None else name
image.savefig(self.log_dir / self.media_dir / name)
def save(self):
self.testtubelogger.save()

1
lib/utils/transforms.py
View File

@ -8,5 +8,4 @@ class AsArray(object):
def __call__(self, x):
array = np.zeros((self.width, self.height))
return array

2
lib/variables.py
View File

@ -5,3 +5,5 @@ HOMOTOPIC = 1
ALTERNATIVE = 0
WHITE = 255
BLACK = 0
DPI = 100

46
lib/visualization/generator_eval.py
View File

@ -1,36 +1,49 @@
import torch
import matplotlib.pyplot as plt
from mpl_toolkits.axisartist.axes_grid import ImageGrid
from tqdm import tqdm
from typing import List
import lib.variables as V
class GeneratorVisualizer(object):
def __init__(self, maps, trajectories, labels, val_result_dict):
# val_results = dict(discriminated_bce_loss, batch_nb, pred_label, label, generated_alternative)
self.generated_alternatives = val_result_dict['generated_alternative']
self.pred_labels = val_result_dict['pred_label']
self.alternatives = val_result_dict['generated_alternative']
self.labels = labels
self.trajectories = trajectories
self.maps = 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)
self._rows = len(self.column_dict_list[0])
def _build_column_dict_list(self):
dict_list = []
for idx in range(self.maps.shape[0]):
image = (self.maps[idx] + self.trajectories[idx] + self.generated_alternatives[idx]).cpu().numpy().squeeze()
label = int(self.labels[idx])
dict_list.append(dict(image=image, label=label))
half_size = int(len(dict_list) // 2)
return dict_list[:half_size], dict_list[half_size:]
trajectories = []
non_hom_alternatives = []
hom_alternatives = []
for idx in range(self.alternatives.shape[0]):
image = (self.alternatives[idx]).cpu().numpy().squeeze()
label = self.labels[idx].item()
if label == V.HOMOTOPIC:
hom_alternatives.append(dict(image=image, label='Homotopic'))
else:
non_hom_alternatives.append(dict(image=image, label='NonHomotopic'))
for idx in range(max(len(hom_alternatives), len(non_hom_alternatives))):
image = (self.maps[idx] + self.trajectories[idx]).cpu().numpy().squeeze()
label = 'original'
trajectories.append(dict(image=image, label=label))
return trajectories, hom_alternatives, non_hom_alternatives
def draw(self):
fig = plt.figure()
padding = 0.25
additional_size = self._cols * padding + 3 * padding
width = (self._map_width * self._cols) / V.DPI + additional_size
height = (self._map_height * self._rows) / V.DPI + additional_size
fig = plt.figure(figsize=(width, height), dpi=V.DPI)
grid = ImageGrid(fig, 111, # similar to subplot(111)
nrows_ncols=(len(self.column_dict_list[0]), len(self.column_dict_list)),
axes_pad=0.2, # pad between axes in inch.
nrows_ncols=(self._rows, self._cols),
axes_pad=padding, # pad between axes in inch.
)
for idx in range(len(grid.axes_all)):
@ -40,4 +53,5 @@ class GeneratorVisualizer(object):
grid[idx].imshow(current_image)
grid[idx].title.set_text(current_label)
fig.cbar_mode = 'single'
fig.tight_layout()
return fig

27
main.py
View File

@ -28,14 +28,16 @@ main_arg_parser = ArgumentParser(description="parser for fast-neural-style")
# Main Parameters
main_arg_parser.add_argument("--main_debug", type=strtobool, default=False, help="")
main_arg_parser.add_argument("--main_eval", type=strtobool, default=False, help="")
main_arg_parser.add_argument("--main_eval", type=strtobool, default=True, help="")
main_arg_parser.add_argument("--main_seed", type=int, default=69, help="")
# Data Parameters
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_dataset_length", type=int, default=100000, 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_normalized", type=strtobool, default=True, help="")
# Transformations
main_arg_parser.add_argument("--transformations_to_tensor", type=strtobool, default=False, help="")
@ -43,16 +45,16 @@ main_arg_parser.add_argument("--transformations_to_tensor", type=strtobool, defa
# Transformations
main_arg_parser.add_argument("--train_outpath", type=str, default="output", help="")
main_arg_parser.add_argument("--train_version", type=strtobool, required=False, help="")
main_arg_parser.add_argument("--train_epochs", type=int, default=10, help="")
main_arg_parser.add_argument("--train_batch_size", type=int, default=256, help="")
main_arg_parser.add_argument("--train_epochs", type=int, default=20, help="")
main_arg_parser.add_argument("--train_batch_size", type=int, default=164, help="")
main_arg_parser.add_argument("--train_lr", type=float, default=0.002, help="")
# Model
main_arg_parser.add_argument("--model_type", type=str, default="CNNRouteGenerator", help="")
main_arg_parser.add_argument("--model_activation", type=str, default="relu", help="")
main_arg_parser.add_argument("--model_activation", type=str, default="elu", help="")
main_arg_parser.add_argument("--model_filters", type=str, default="[32, 16, 4]", help="")
main_arg_parser.add_argument("--model_classes", type=int, default=2, help="")
main_arg_parser.add_argument("--model_lat_dim", type=int, default=2, help="")
main_arg_parser.add_argument("--model_lat_dim", type=int, default=4, 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_dropout", type=float, default=0.00, help="")
@ -93,10 +95,10 @@ def run_lightning_loop(config_obj):
# =============================================================================
# Init
model: LightningBaseModule = config_obj.model_class(config_obj.model_paramters)
model.init_weights()
model.init_weights(torch.nn.init.xavier_normal_)
if model.name == 'CNNRouteGeneratorDiscriminated':
# ToDo: Make this dependent on the used seed
path = Path(Path(config_obj.train.outpath) / 'classifier_cnn' / 'trained')
path = Path(Path(config_obj.train.outpath) / 'classifier_cnn' / 'version_0')
disc_model = SavedLightningModels.load_checkpoint(path).restore()
model.set_discriminator(disc_model)
@ -107,14 +109,14 @@ def run_lightning_loop(config_obj):
weights_save_path=logger.log_dir,
gpus=[0] if torch.cuda.is_available() else None,
check_val_every_n_epoch=1,
num_sanity_val_steps=0,
# 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
checkpoint_callback=checkpoint_callback,
logger=logger,
val_percent_check=0.05,
fast_dev_run=config_obj.main.debug,
early_stop_callback=None,
val_percent_check=0.10,
num_sanity_val_steps=1,
early_stop_callback=None
)
# Train It
@ -125,7 +127,8 @@ def run_lightning_loop(config_obj):
model.save_to_disk(logger.log_dir)
# Evaluate It
trainer.test()
if config_obj.main.eval:
trainer.test()
return model

2
multi_run.py
View File

@ -18,7 +18,7 @@ if __name__ == '__main__':
# use_bias, activation, model, use_norm, max_epochs, filters
cnn_classifier = dict(train_epochs=10, model_use_bias=True, model_use_norm=True, model_activation='leaky_relu',
model_type='classifier_cnn', model_filters=[16, 32, 64], data_batchsize=512)
# use_bias, activation, model, use_norm, max_epochs, sr, lat_dim, filters
# use_bias, activation, model, use_norm, max_epochs, sr, feature_mixed_dim, filters
for arg_dict in [cnn_classifier]:
for seed in range(5):