def main_flags():
# Data & model config
flags.DEFINE_string('data_config', 'datasets/gqn_config.py',
'Path to a data config file.')
flags.DEFINE_string('model_config', 'models/genesis_config.py',
'Path to a model config file.')
# Trained model
flags.DEFINE_string('model_dir', 'checkpoints/test/1',
'Path to model directory.')
flags.DEFINE_string('model_file', 'model.ckpt-FINAL', 'Name of model file.')
# FID
flags.DEFINE_integer('feat_dim', 2048, 'Number of Incpetion features.')
flags.DEFINE_integer('num_fid_images', 10000,
'Number of images to compute the FID on.')
# Other
flags.DEFINE_string('img_dir', '/tmp', 'Directory for saving pngs.')
flags.DEFINE_integer('batch_size', 10, 'Mini-batch size.')
flags.DEFINE_boolean('gpu', True, 'Use GPU if available.')
flags.DEFINE_integer('seed', 0, 'Seed for random number generators.')
def main_flags():
# Data & model config
flags.DEFINE_string('data_config', 'datasets/multid_config.py',
'Path to a data config file.')
flags.DEFINE_string('model_config', 'models/genesis_config.py',
'Path to a model config file.')
# Logging config
flags.DEFINE_string('results_dir', 'checkpoints',
'Top directory for all experimental results.')
flags.DEFINE_string('run_name', 'test',
'Name of this job and name of results folder.')
flags.DEFINE_integer(
'report_loss_every', 1000,
'Number of iterations between reporting minibatch loss.')
flags.DEFINE_integer('run_validation_every', 10000,
'How many equally spaced validation runs to do.')
flags.DEFINE_integer('num_checkpoints', 40,
'How many equally spaced model checkpoints to save.')
flags.DEFINE_boolean('resume', False, 'Tries to resume a job if True.')
flags.DEFINE_boolean(
'log_grads_and_weights', False,
'Log gradient and weight histograms - storage intensive!')
flags.DEFINE_boolean(
'log_distributions', False,
'Log mu and sigma of posterior and prior distributions.')
# Optimisation config
flags.DEFINE_integer('train_iter', 2000000,
'Number of training iterations.')
flags.DEFINE_integer('batch_size', 32, 'Mini-batch size.')
flags.DEFINE_string('optimiser', 'adam', 'Optimiser for updating weights.')
flags.DEFINE_float('learning_rate', 0.0001, 'Learning rate.')
flags.DEFINE_integer('N_eval', 10000,
'Number of samples to run evaluation on.')
# Loss config
flags.DEFINE_float('beta', 0.5, 'KL weighting.')
flags.DEFINE_boolean('beta_warmup', True, 'Warm up beta.')
flags.DEFINE_boolean('geco', True, 'Use GECO objective.')
flags.DEFINE_float('g_goal', 0.5655, 'GECO recon goal.')
flags.DEFINE_float('g_lr', 1e-5, 'GECO learning rate.')
flags.DEFINE_float('g_alpha', 0.99, 'GECO momentum for error.')
flags.DEFINE_float('g_init', 1.0, 'GECO inital Lagrange factor.')
flags.DEFINE_float('g_min', 1e-10, 'GECO min Lagrange factor.')
flags.DEFINE_float('g_speedup', 10., 'Scale GECO lr if delta positive.')
# Other
flags.DEFINE_boolean('gpu', True, 'Use GPU if available.')
flags.DEFINE_boolean('multi_gpu', False, 'Use multiple GPUs if available.')
flags.DEFINE_boolean('debug', False, 'Debug flag.')
flags.DEFINE_integer('seed', 0, 'Seed for random number generators.')
import modules.seq_att as seq_att
import modules.decoders as decoders
from modules.component_vae import ComponentVAE
from third_party.sylvester.VAE import VAE
import utils.misc as misc
# Model type
flags.DEFINE_boolean('two_stage', True, 'Use two stages if two, else only one.')
# Priors
flags.DEFINE_boolean('autoreg_prior', True, 'Autoregressive prior.')
flags.DEFINE_boolean('comp_prior', True, 'Component prior.')
# Attention VAE
flags.DEFINE_integer('attention_latents', 64, 'Latent dimension.')
flags.DEFINE_string('enc_norm', 'bn', '{bn, in} - norm type in encoder.')
flags.DEFINE_string('dec_norm', 'bn', '{bn, in} - norm type in decoder.')
# Component VAE
flags.DEFINE_integer('comp_enc_channels', 32, 'Starting number of channels.')
flags.DEFINE_integer('comp_ldim', 16, 'Latent dimension of the VAE.')
flags.DEFINE_integer('comp_dec_channels', 32, 'Num channels in Broadcast Decoder.')
flags.DEFINE_integer('comp_dec_layers', 4, 'Num layers in Broadcast Decoder.')
# Losses
flags.DEFINE_boolean('pixel_bound', True, 'Bound pixel values to [0, 1].')
flags.DEFINE_float('pixel_std1', 0.7, 'StdDev of reconstructed pixels.')
flags.DEFINE_float('pixel_std2', 0.7, 'StdDev of reconstructed pixels.')
flags.DEFINE_boolean('montecarlo_kl', True, 'Evaluate KL via MC samples.')
def load(cfg):
import torch
from lie_conv.dynamicsTrainer import FC, HFC
from lie_conv.graphnets import OGN, HOGN
from eqv_transformer.dynamics_predictor import DynamicsPredictor
from forge import flags
flags.DEFINE_integer("channel_width", 256, "Channel width for the network.")
flags.DEFINE_integer("num_layers", 4, "Number of layers.")
flags.DEFINE_integer("model_seed", 0, "Model rng seed")
flags.DEFINE_string(
"network_type",
"FC",
"One of FC, HFC, OGN, HOGN.",
)
def load(config, **unused_kwargs):
print(f"Using network: {config.network_type}.")
torch.manual_seed(config.model_seed)
network = (eval(config.network_type))(
sys_dim=config.sys_dim,
d=config.space_dim,
k=config.channel_width,
num_layers=config.num_layers,
)
import os
import torch
from torch.utils.data import DataLoader
from oil.utils.utils import FixedNumpySeed, islice
from oil.datasetup.datasets import split_dataset
from lie_conv.datasets import SpringDynamics
from forge import flags
flags.DEFINE_integer("n_train", 3000, "Number of training datapoints.")
flags.DEFINE_integer("n_test", 2000, "Number of testing datapoints.")
flags.DEFINE_integer("n_val", 2000, "Number of validation datapoints.")
flags.DEFINE_integer("n_systems", 10000, "Size of total dataset generated.")
flags.DEFINE_string(
"data_path",
"./datasets/ODEDynamics/SpringDynamics/",
"Dataset is loaded from and/or downloaded to this path.",
)
flags.DEFINE_integer("sys_dim", 2, "[add description].")
flags.DEFINE_integer("space_dim", 2, "Dimension of particle system.")
flags.DEFINE_integer("data_seed", 0, "Data splits random seed.")
flags.DEFINE_integer("num_particles", 6, "Number of particles in system.")
flags.DEFINE_integer("chunk_len", 5, "Length of trajectories.")
flags.DEFINE_boolean(
"load_preprocessed",
False,
"Load data already preprocessed to avoid RAM memory spike. Ensure data exists first for the chunk_lun required.",
)
from torch.distributions.normal import Normal
from torch.distributions.categorical import Categorical
from torch.distributions.kl import kl_divergence
import numpy as np
from forge import flags
from modules.unet import UNet
import modules.seq_att as seq_att
from modules.component_vae import ComponentVAE
from models.genesis_config import Genesis
from utils import misc
# Attention network
flags.DEFINE_integer('filter_start', 32,
'Starting number of channels in UNet.')
flags.DEFINE_string('prior_mode', 'softmax', '{scope, softmax}')
def load(cfg):
return MONet(cfg)
class MONet(nn.Module):
def __init__(self, cfg):
super(MONet, self).__init__()
# Configuration
self.K_steps = cfg.K_steps
self.prior_mode = cfg.prior_mode
self.mckl = cfg.montecarlo_kl
self.debug = cfg.debug
# =========================== A2I Copyright Header ===========================
from attrdict import AttrDict
import torch
import torch.nn as nn
from torch.distributions.normal import Normal
from forge import flags
from modules.blocks import Flatten
from modules.decoders import BroadcastDecoder
from third_party.sylvester.VAE import VAE
# GatedConvVAE
flags.DEFINE_integer('latent_dimension', 64, 'Latent channels.')
flags.DEFINE_boolean('broadcast_decoder', False,
'Use broadcast decoder instead of deconv.')
# Losses
flags.DEFINE_boolean('pixel_bound', True, 'Bound pixel values to [0, 1].')
flags.DEFINE_float('pixel_std', 0.7, 'StdDev of reconstructed pixels.')
def load(cfg):
return BaselineVAE(cfg)
class BaselineVAE(nn.Module):
def __init__(self, cfg):
super(BaselineVAE, self).__init__()
cfg.K_steps = None
from torchvision import transforms
import numpy as np
from PIL import Image
from forge import flags
from forge.experiment_tools import fprint
from utils.misc import loader_throughput
from third_party.shapestacks.shapestacks_provider import _get_filenames_with_labels
flags.DEFINE_string('data_folder', 'data/shapestacks', 'Path to data folder.')
flags.DEFINE_string('split_name', 'default', '{default, blocks_all, css_all}')
flags.DEFINE_integer('img_size', 64, 'Dimension of images. Images are square.')
flags.DEFINE_boolean('shuffle_test', False, 'Shuffle test set.')
flags.DEFINE_integer('num_workers', 4, 'Number of threads for loading data.')
flags.DEFINE_boolean('copy_to_tmp', False, 'Copy files to /tmp.')
flags.DEFINE_integer('K_steps', 9, 'Number of recurrent steps.')
MAX_SHAPES = 6
CENTRE_CROP = 196
def load(cfg, **unused_kwargs):
del unused_kwargs
if not os.path.exists(cfg.data_folder):
from attrdict import AttrDict
import torch
from torch import nn
import torch.nn.functional as F
from lie_conv.datasets import SE3aug
from eqv_transformer.attention import SetTransformer
from eqv_transformer.molecule_predictor import MoleculePredictor
from forge import flags
flags.DEFINE_boolean("data_augmentation", False,
"Apply data augmentation to the input data or not")
flags.DEFINE_integer("n_enc_layers", 4, "Number of encoder layers.")
flags.DEFINE_integer("n_dec_layers", 4, "Number of encoder layers.")
flags.DEFINE_integer("num_heads", 4, "Number of attention heads.")
flags.DEFINE_integer(
"n_inducing_points",
0,
"Number of inducing points; does not use inducing points if 0.",
)
flags.DEFINE_boolean("layer_norm", False, "Uses layer-norm if True.")
flags.DEFINE_integer("hidden_dim", 128, "Hidden dimension between layers")
class MolecueSetTransformer(SetTransformer):
def __init__(self, num_species, charge_scale, aug=False, **kwargs):
super().__init__(dim_input=3 + 3 * num_species,
num_outputs=1,
# modification, is not permitted without an explicit licensing agreement
# (research or commercial). No warranty, explicit or implicit, provided.
#
# =========================== A2I Copyright Header ===========================
import os
from pathlib import Path
import torch
import torch.nn.functional as F
from forge import flags
from forge.experiment_tools import fprint
from torchvision.io import read_video
from utils.misc import loader_throughput
flags.DEFINE_integer("img_size", 64, "Dimension of images. Images are square.")
flags.DEFINE_integer(
"val_frac", 60, "Fraction of training images to use for validation."
)
flags.DEFINE_integer("num_workers", 4, "TF records dataset.")
flags.DEFINE_integer("buffer_size", 128, "TF records dataset.")
flags.DEFINE_integer("K_steps", 7, "Number of recurrent steps.")
SEED = 0
def load(cfg, **unused_kwargs):
# Fix TensorFlow seed
import torch
from lie_conv.moleculeTrainer import MolecLieResNet
from eqv_transformer.molecule_predictor import MoleculePredictor
from lie_conv.lieGroups import SE3, SO3, T, Trivial
from forge import flags
flags.DEFINE_bool(
"data_augmentation",
False,
"Apply data augmentation to the data before passing to the model",
)
flags.DEFINE_integer(
"nbhd_size", 25, "The number of samples to use for Monte Carlo estimation")
flags.DEFINE_string("activation_function", "swish",
"Activation function to use in the network")
flags.DEFINE_boolean("batch_norm", True, "Use batch norm in the layers")
flags.DEFINE_bool(
"mean_pooling",
True,
"Use mean pooling insteave of sum pooling in the invariant layer",
)
flags.DEFINE_integer("num_layers", 6, "Number of ResNet layers to use")
flags.DEFINE_string("group", "SE3", "Group to be invariant to")
flags.DEFINE_integer("channels", 1536, "Number of channels in the conv layers")
flags.DEFINE_float(
"fill",
1.0,
"specifies the fraction of the input which is included in local neighborhood. (can be array to specify a different value for each layer",
import forge.experiment_tools as fet
from forge.experiment_tools import fprint
from utils.plotting import plot
# Data & model config
flags.DEFINE_string('data_config', 'datasets/gqn_config.py',
'Path to a data config file.')
flags.DEFINE_string('model_config', 'models/genesis_config.py',
'Path to a model config file.')
# Trained model
flags.DEFINE_string('model_dir', 'checkpoints/test/1',
'Path to model directory.')
flags.DEFINE_string('model_file', 'model.ckpt-FINAL', 'Name of model file.')
# Other
flags.DEFINE_integer('num_images', 10, 'Number of images to visualize.')
def main():
# Parse flags
config = forge.config()
fet.print_flags()
# Restore flags of pretrained model
flag_path = osp.join(config.model_dir, 'flags.json')
fprint(f"Restoring flags from {flag_path}")
pretrained_flags = AttrDict(fet.json_load(flag_path))
pretrained_flags.debug = True
# Fix seeds. Always first thing to be done after parsing the config!
torch.manual_seed(0)
np.random.seed(0)
from attrdict import AttrDict
import torch
from torch import nn
import torch.nn.functional as F
from eqv_transformer.classfier import Classifier
from eqv_transformer.attention import SetTransformer
from forge import flags
flags.DEFINE_integer("input_dim", 2, "Dimensionality of the input.")
flags.DEFINE_integer("n_outputs", 4, "Number of output vectors.")
flags.DEFINE_integer("output_dim", 3, "Dimensionality of the output.")
flags.DEFINE_integer("n_enc_layers", 4, "Number of encoder layers.")
flags.DEFINE_integer("n_dec_layers", 4, "Number of encoder layers.")
flags.DEFINE_integer("num_heads", 4, "Number of attention heads.")
flags.DEFINE_integer(
"n_inducing_points",
0,
"Number of inducing points; does not use inducing points if 0.",
)
flags.DEFINE_boolean("layer_norm", False, "Uses layer-norm if True.")
def load(config, **unused_kwargs):
del unused_kwargs
encoder = SetTransformer(
config.input_dim,
config.n_outputs,
config.output_dim,
)
flags.DEFINE_string(
"model_config",
"configs/dynamics/eqv_transformer_model.py",
"Path to a model config file.",
)
# Job management
flags.DEFINE_string(
"run_name",
"test",
"Name of this job and name of results folder.",
)
flags.DEFINE_boolean("resume", False, "Tries to resume a job if True.")
# Logging
flags.DEFINE_integer("report_loss_every", 10,
"Number of iterations between reporting minibatch loss.")
flags.DEFINE_integer(
"evaluate_every", 10000,
"Number of iterations between reporting validation loss.")
flags.DEFINE_integer(
"save_check_points",
50,
"frequency with which to save checkpoints, in number of epoches.",
)
flags.DEFINE_boolean("log_train_values", True, "Logs train values if True.")
# Optimization
flags.DEFINE_integer("train_epochs", 200, "Maximum number of training epochs.")
flags.DEFINE_integer("batch_size", 100, "Mini-batch size.")
flags.DEFINE_float("learning_rate", 1e-3, "Adam learning rate.")
flags.DEFINE_float("beta1", 0.9, "Adam Beta 1 parameter")
import forge
from forge import flags
flags.DEFINE_float(
"subsample_trainset",
1.0,
"Proportion or number of samples of the full trainset to use",
)
flags.DEFINE_string(
"task",
"h**o",
"Which task in the QM9 dataset to train on. Pass as a comma separated string",
)
flags.DEFINE_boolean("recenter", False,
"Recenter the positions of atoms with charge > 0")
flags.DEFINE_integer("batch_fit", 0, "number of samples to fit to")
flags.DEFINE_integer("data_seed", 0, "seed to pick data with")
def load(config, **unused_kwargs):
with FixedNumpySeed(config.data_seed):
datasets, num_species, charge_scale = QM9datasets(
os.path.join(config.data_dir, "qm9"))
if config.subsample_trainset != 1.0:
datasets.update(
split_dataset(datasets["train"],
{"train": config.subsample_trainset}))
if config.batch_fit != 0:
datasets.update(
split_dataset(datasets["train"], {"train": config.batch_fit}))
import forge.experiment_tools as fet
from forge.experiment_tools import fprint
from utils.misc import average_ari, average_segcover
# Config
flags.DEFINE_string('data_config', 'datasets/shapestacks_config.py',
'Path to a data config file.')
flags.DEFINE_string('model_config', 'models/genesis_config.py',
'Path to a model config file.')
# Trained model
flags.DEFINE_string('model_dir', 'checkpoints/test/1',
'Path to model directory.')
flags.DEFINE_string('model_file', 'model.ckpt-FINAL', 'Name of model file.')
# Other
flags.DEFINE_integer('seed', 0, 'Seed for random number generators.')
flags.DEFINE_integer('num_images', 300, 'Number of images to run on.')
flags.DEFINE_string('split', 'test', '{train, val, test}')
# Set manual seed
torch.manual_seed(0)
np.random.seed(0)
random.seed(0)
# Make CUDA operations deterministic
torch.backends.cudnn.deterministic = True
torch.backends.cudnn.benchmark = False
def main():
# Parse flags
config = forge.config()
import tensorflow as tf
import forge
from forge import flags
import forge.experiment_tools as fet
# job config
flags.DEFINE_string('data_config', 'configs/mnist_data.py', 'Path to a data config file.')
flags.DEFINE_string('model_config', 'configs/mnist_mlp.py', 'Path to a model config file.')
flags.DEFINE_string('results_dir', '../checkpoints', 'Top directory for all experimental results.')
flags.DEFINE_string('run_name', 'test_run', 'Name of this job. Results will be stored in a corresponding folder.')
flags.DEFINE_boolean('resume', False, 'Tries to resume a job if True.')
# logging config
flags.DEFINE_integer('report_loss_every', int(1e3), 'Number of iterations between reporting minibatch loss - hearbeat.')
flags.DEFINE_integer('save_itr', int(1e4), 'Number of iterations between snapshotting the model.')
flags.DEFINE_integer('train_itr', int(2e6), 'Maximum number of training iterations.')
# experiment config
flags.DEFINE_integer('batch_size', 32, '')
flags.DEFINE_float('learning_rate', 1e-5, 'Initial values of the learning rate')
# gpu
flags.DEFINE_string('gpu', '0', 'Id of the gpu to use for this job.')
# Parse flags
config = forge.config()
# sets visible gpus to config.gpu
fet.set_gpu(config.gpu)
from eqv_transformer.classfier import Classifier
from eqv_transformer.eqv_attention import EquivariantTransformer
from lie_conv.lieGroups import SE3, SE2, SO3, T, Trivial
# from lie_conv.datasets import SE3aug
from forge import flags
flags.DEFINE_boolean(
"data_augmentation",
False,
"Apply data augmentation to the data before passing to the model",
)
flags.DEFINE_integer("dim_hidden", 512, "Dimension of features to use in each layer")
flags.DEFINE_string(
"activation_function", "swish", "Activation function to use in the network"
)
# flags.DEFINE_boolean("layer_norm", True, "Use layer norm in the layers")
flags.DEFINE_boolean(
"mean_pooling",
True,
"Use mean pooling insteave of sum pooling in the invariant layer",
)
flags.DEFINE_integer("num_heads", 8, "Number of attention heads in each layer")
flags.DEFINE_integer("kernel_dim", 16, "Hidden layer size to use in kernel MLPs")
# flags.DEFINE_boolean("batch_norm", False, "Use batch norm in the kernel MLPs")
flags.DEFINE_integer("num_layers", 6, "Number of ResNet layers to use")
flags.DEFINE_string("group", "SE2", "Group to be invariant to")
flags.DEFINE_integer(
import torch
from eqv_transformer.eqv_attention import EquivariantTransformer
from eqv_transformer.molecule_predictor import MoleculePredictor
from lie_conv.lieGroups import SE3, SO3, T, Trivial
from lie_conv.datasets import SE3aug
from forge import flags
flags.DEFINE_boolean(
"data_augmentation",
True,
"Apply data augmentation to the data before passing to the model",
)
flags.DEFINE_integer("dim_hidden", 512,
"Dimension of features to use in each layer")
flags.DEFINE_string("activation_function", "swish",
"Activation function to use in the network")
flags.DEFINE_boolean(
"mean_pooling",
True,
"Use mean pooling insteave of sum pooling in the invariant layer",
)
flags.DEFINE_integer("num_heads", 8, "Number of attention heads in each layer")
flags.DEFINE_string(
"block_norm",
"layer_pre",
"Type of norm to use in the attention block. none/[layer/batch]_[pre/post]",
)
flags.DEFINE_string(
"output_norm",
#
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU General Public License for more details.
# You should have received a copy of the GNU General Public License
# along with this program. If not, see <http://www.gnu.org/licenses/>.
#
########################################################################################
"""Simple MLP model config for MNIST classification."""
import sonnet as snt
import tensorflow as tf
from forge import flags
flags.DEFINE_integer('n_hidden', 128, 'Number of hidden units.')
def load(config, **inputs):
imgs, labels = inputs['train_img'], inputs['train_label']
imgs = snt.BatchFlatten()(imgs)
mlp = snt.nets.MLP([config.n_hidden, 10])
logits = mlp(imgs)
labels = tf.cast(labels, tf.int32)
loss = tf.reduce_mean(
tf.nn.sparse_softmax_cross_entropy_with_logits(logits=logits,
labels=labels))
import torch
import torch.nn.functional as F
import tensorflow as tf
import numpy as np
import third_party.tf_gqn.gqn_tfr_provider as gqn
from forge import flags
from forge.experiment_tools import fprint
from utils.misc import loader_throughput
flags.DEFINE_string('data_folder', 'data/gqn_datasets', 'Path to data folder.')
flags.DEFINE_integer('img_size', 64, 'Dimension of images. Images are square.')
flags.DEFINE_integer('val_frac', 60,
'Fraction of training images to use for validation.')
flags.DEFINE_integer('num_workers', 4, 'TF records dataset.')
flags.DEFINE_integer('buffer_size', 128, 'TF records dataset.')
flags.DEFINE_integer('K_steps', 7, 'Number of recurrent steps.')
SEED = 0
def load(cfg, **unused_kwargs):
# Fix TensorFlow seed
global SEED
SEED = cfg.seed
# =========================== A2I Copyright Header ===========================
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.colors import NoNorm
import torch
import forge
from forge import flags
import forge.experiment_tools as fet
# Config
flags.DEFINE_string('data_config', 'datasets/multid_config.py',
'Path to a data config file.')
flags.DEFINE_integer('batch_size', 8, 'Mini-batch size.')
flags.DEFINE_integer('seed', 0, 'Seed for random number generators.')
def main():
# Parse flags
cfg = forge.config()
cfg.num_workers = 0
# Set manual seed
torch.manual_seed(cfg.seed)
np.random.seed(cfg.seed)
# Make CUDA operations deterministic
torch.backends.cudnn.deterministic = True
torch.backends.cudnn.benchmark = False
from attrdict import AttrDict
import torch
from torch import nn
import torch.nn.functional as F
from eqv_transformer.classfier import Classifier
from eqv_transformer.eqv_attention_se2_finite import EqvTransformer
from forge import flags
# flags.DEFINE_integer('input_dim', 2, 'Dimensionality of the input.')
flags.DEFINE_integer('n_outputs', 4, 'Number of output vectors.')
# flags.DEFINE_integer('output_dim', 3, 'Dimensionality of the output.')
flags.DEFINE_string('content_type', 'pairwise_distances',
'How to initialize y')
flags.DEFINE_integer('n_enc_layers', 4, 'Number of encoder layers.')
flags.DEFINE_integer('n_dec_layers', 4, 'Number of encoder layers.')
flags.DEFINE_integer('n_heads', 4, 'Number of attention heads.')
flags.DEFINE_boolean('layer_norm', False, 'Uses layer-norm if True.')
flags.DEFINE_integer('cn', 5, 'Size of rotation group.')
flags.DEFINE_string('similarity_fn', 'softmax',
'Similarity function used to compute attention weights.')
flags.DEFINE_string('arch', 'set_transf', 'Architecture.')
flags.DEFINE_integer('num_moments', 5,
'When using pairwise distances as Y, number of moments.')
def load(config, **unused_kwargs):
del unused_kwargs
# should not affect things #### number of moments # config.patterns_reps * 17 - 1
from forge import flags
from forge.experiment_tools import fprint
from utils.misc import loader_throughput, len_tfrecords, np_img_centre_crop
import third_party.multi_object_datasets.multi_dsprites as multi_dsprites
import third_party.multi_object_datasets.objects_room as objects_room
import third_party.multi_object_datasets.clevr_with_masks as clevr_with_masks
import third_party.multi_object_datasets.tetrominoes as tetrominoes
flags.DEFINE_string('data_folder', 'data/multi-object-datasets',
'Path to data folder.')
flags.DEFINE_string('dataset', 'objects_room',
'{multi_dsprites, objects_room, clevr, tetrominoes}')
flags.DEFINE_integer('img_size', -1, 'Dimension of images. Images are square.')
flags.DEFINE_integer('dataset_size', -1, 'Number of images to use.')
flags.DEFINE_integer('num_workers', 4, 'Number of threads for loading data.')
flags.DEFINE_integer('buffer_size', 128, 'TF records dataset.')
flags.DEFINE_integer('K_steps', -1, 'Number of recurrent steps.')
MULTI_DSPRITES = '/multi_dsprites/multi_dsprites_colored_on_colored.tfrecords'
OBJECTS_ROOM = '/objects_room/objects_room_train.tfrecords'
CLEVR = '/clevr_with_masks/clevr_with_masks_train.tfrecords'
TETROMINOS = '/tetrominoes/tetrominoes_train.tfrecords'
CLEVR_CROP = 192 # Following pre-processing in the IODINE paper
SEED = 0
# Configuration files to load
flags.DEFINE_string(
"data_config", "configs/molecule/qm9_data.py", "Path to a data config file."
)
flags.DEFINE_string(
"model_config",
"configs/molecule/set_transformer.py",
"Path to a model config file.",
)
# Job management
flags.DEFINE_string("run_name", "test", "Name of this job and name of results folder.")
flags.DEFINE_boolean("resume", False, "Tries to resume a job if True.")
# Logging
flags.DEFINE_integer(
"report_loss_every", 500, "Number of iterations between reporting minibatch loss."
)
flags.DEFINE_integer(
"evaluate_every", 10000, "Number of iterations between reporting validation loss."
)
flags.DEFINE_integer(
"save_check_points",
10,
"frequency with which to save checkpoints, in number of epochs.",
)
flags.DEFINE_boolean("log_train_values", True, "Logs train values if True.")
flags.DEFINE_float(
"ema_alpha", 0.99, "Alpha coefficient for exponential moving average of train logs."
)
# Optimization
from forge import flags
import forge.experiment_tools as fet
# Job config
flags.DEFINE_string('data_config', 'configs/mnist_data.py',
'Path to a data config file.')
flags.DEFINE_string('model_config', 'configs/mnist_mlp.py',
'Path to a model config file.')
flags.DEFINE_string('results_dir', 'checkpoints',
'Top directory for all experimental results.')
flags.DEFINE_string('run_name', 'mnist',
'Name of this job and name of results folder.')
flags.DEFINE_boolean('resume', False, 'Tries to resume a job if True.')
# Logging config
flags.DEFINE_integer('report_loss_every', 100,
'Number of iterations between reporting minibatch loss.')
flags.DEFINE_integer('train_epochs', 20, 'Maximum number of training epochs.')
# Experiment config
flags.DEFINE_integer('batch_size', 32, 'Mini-batch size.')
flags.DEFINE_float('learning_rate', 1e-5, 'SGD learning rate.')
# Parse flags
config = forge.config()
# Prepare enviornment
logdir = osp.join(config.results_dir, config.run_name)
logdir, resume_checkpoint = fet.init_checkpoint(logdir, config.data_config,
config.model_config,
config.resume)
checkpoint_name = osp.join(logdir, 'model.ckpt')
"""
import collections
import functools
import numpy as np
import tensorflow as tf
import torch
from torchvision import transforms
# import ipdb
from forge import flags
import pickle
import os
import json
flags.DEFINE_integer("train_size", 10000, "Number of training examples per epoch.")
flags.DEFINE_integer("test_size", 1000, "Number of testing examples per epoch.")
flags.DEFINE_integer("naug", 2, "Number of augmentation.")
flags.DEFINE_float("corner_noise", 0.1, "See `create_constellations`.")
flags.DEFINE_boolean("shuffle_corners", True, "See `create_constellations`.")
flags.DEFINE_float("pattern_upscale", 0.0, "See `create_constellations`.")
flags.DEFINE_float("max_rotation", 0.33, "See `create_constellations`.")
flags.DEFINE_float("global_rotation_angle", 0.0, "See `create_constellations`.")
flags.DEFINE_float("global_translation", 0.0, "See `create_constellations`.")
flags.DEFINE_float("pattern_drop_prob", 0.5, "See `create_constellations`.")
flags.DEFINE_integer("patterns_reps", 2, "See `create_constellations`.")
flags.DEFINE_integer("data_seed", 0, "Seed for data generation.")
def roots_of_unity(n):
