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utils.py
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utils.py
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#!/usr/bin/env python
# -*- coding: utf-8 -*-
''' Utilities file
This file contains utility functions for bookkeeping, logging, and data loading.
Methods which directly affect training should either go in layers, the model,
or train_fns.py.
'''
from __future__ import print_function
import sys
import os
import numpy as np
import time
import datetime
import json
import pickle
from argparse import ArgumentParser
import animal_hash
import torch
import torch.nn as nn
import torch.nn.functional as F
import torchvision
import torchvision.transforms as transforms
from torch.utils.data import DataLoader
import datasets as dset
import pdb
def prepare_parser():
usage = 'Parser for all scripts.'
parser = ArgumentParser(description=usage)
### Dataset/Dataloader stuff ###
parser.add_argument(
'--dataset', type=str, default='I128_hdf5',
help='Which Dataset to train on, out of I128, I256, C10, C100;'
'Append "_hdf5" to use the hdf5 version for ISLVRC '
'(default: %(default)s)')
parser.add_argument(
'--augment', action='store_true', default=False,
help='Augment with random crops and flips (default: %(default)s)')
parser.add_argument(
'--num_workers', type=int, default=8,
help='Number of dataloader workers; consider using less for HDF5 '
'(default: %(default)s)')
parser.add_argument(
'--no_pin_memory', action='store_false', dest='pin_memory', default=True,
help='Pin data into memory through dataloader? (default: %(default)s)')
parser.add_argument(
'--shuffle', action='store_true', default=False,
help='Shuffle the data (strongly recommended)? (default: %(default)s)')
parser.add_argument(
'--load_in_mem', action='store_true', default=False,
help='Load all data into memory? (default: %(default)s)')
parser.add_argument(
'--use_multiepoch_sampler', action='store_true', default=False,
help='Use the multi-epoch sampler for dataloader? (default: %(default)s)')
### Model stuff ###
parser.add_argument(
'--model', type=str, default='BigGAN',
help='Name of the model module (default: %(default)s)')
parser.add_argument(
'--G_param', type=str, default='SN',
help='Parameterization style to use for G, spectral norm (SN) or SVD (SVD)'
' or None (default: %(default)s)')
parser.add_argument(
'--D_param', type=str, default='SN',
help='Parameterization style to use for D, spectral norm (SN) or SVD (SVD)'
' or None (default: %(default)s)')
parser.add_argument(
'--G_ch', type=int, default=64,
help='Channel multiplier for G (default: %(default)s)')
parser.add_argument(
'--D_ch', type=int, default=64,
help='Channel multiplier for D (default: %(default)s)')
parser.add_argument(
'--G_depth', type=int, default=1,
help='Number of resblocks per stage in G? (default: %(default)s)')
parser.add_argument(
'--D_depth', type=int, default=1,
help='Number of resblocks per stage in D? (default: %(default)s)')
parser.add_argument(
'--D_thin', action='store_false', dest='D_wide', default=True,
help='Use the SN-GAN channel pattern for D? (default: %(default)s)')
parser.add_argument(
'--G_shared', action='store_true', default=False,
help='Use shared embeddings in G? (default: %(default)s)')
parser.add_argument(
'--shared_dim', type=int, default=0,
help='G''s shared embedding dimensionality; if 0, will be equal to dim_z. '
'(default: %(default)s)')
parser.add_argument(
'--dim_z', type=int, default=128,
help='Noise dimensionality: %(default)s)')
parser.add_argument(
'--z_var', type=float, default=1.0,
help='Noise variance: %(default)s)')
parser.add_argument(
'--hier', action='store_true', default=False,
help='Use hierarchical z in G? (default: %(default)s)')
parser.add_argument(
'--cross_replica', action='store_true', default=False,
help='Cross_replica batchnorm in G?(default: %(default)s)')
parser.add_argument(
'--mybn', action='store_true', default=False,
help='Use my batchnorm (which supports standing stats?) %(default)s)')
parser.add_argument(
'--G_nl', type=str, default='relu',
help='Activation function for G (default: %(default)s)')
parser.add_argument(
'--D_nl', type=str, default='relu',
help='Activation function for D (default: %(default)s)')
parser.add_argument(
'--G_attn', type=str, default='64',
help='What resolutions to use attention on for G (underscore separated) '
'(default: %(default)s)')
parser.add_argument(
'--D_attn', type=str, default='64',
help='What resolutions to use attention on for D (underscore separated) '
'(default: %(default)s)')
parser.add_argument(
'--norm_style', type=str, default='bn',
help='Normalizer style for G, one of bn [batchnorm], in [instancenorm], '
'ln [layernorm], gn [groupnorm] (default: %(default)s)')
### Model init stuff ###
parser.add_argument(
'--seed', type=int, default=0,
help='Random seed to use; affects both initialization and '
' dataloading. (default: %(default)s)')
parser.add_argument(
'--G_init', type=str, default='ortho',
help='Init style to use for G (default: %(default)s)')
parser.add_argument(
'--D_init', type=str, default='ortho',
help='Init style to use for D(default: %(default)s)')
parser.add_argument(
'--skip_init', action='store_true', default=False,
help='Skip initialization, ideal for testing when ortho init was used '
'(default: %(default)s)')
### Optimizer stuff ###
parser.add_argument(
'--G_lr', type=float, default=5e-5,
help='Learning rate to use for Generator (default: %(default)s)')
parser.add_argument(
'--D_lr', type=float, default=2e-4,
help='Learning rate to use for Discriminator (default: %(default)s)')
parser.add_argument(
'--G_B1', type=float, default=0.0,
help='Beta1 to use for Generator (default: %(default)s)')
parser.add_argument(
'--D_B1', type=float, default=0.0,
help='Beta1 to use for Discriminator (default: %(default)s)')
parser.add_argument(
'--G_B2', type=float, default=0.999,
help='Beta2 to use for Generator (default: %(default)s)')
parser.add_argument(
'--D_B2', type=float, default=0.999,
help='Beta2 to use for Discriminator (default: %(default)s)')
### Batch size, parallel, and precision stuff ###
parser.add_argument(
'--batch_size', type=int, default=64,
help='Default overall batchsize (default: %(default)s)')
parser.add_argument(
'--G_batch_size', type=int, default=0,
help='Batch size to use for G; if 0, same as D (default: %(default)s)')
parser.add_argument(
'--num_G_accumulations', type=int, default=1,
help='Number of passes to accumulate G''s gradients over '
'(default: %(default)s)')
parser.add_argument(
'--num_D_steps', type=int, default=2,
help='Number of D steps per G step (default: %(default)s)')
parser.add_argument(
'--num_D_accumulations', type=int, default=1,
help='Number of passes to accumulate D''s gradients over '
'(default: %(default)s)')
parser.add_argument(
'--split_D', action='store_true', default=False,
help='Run D twice rather than concatenating inputs? (default: %(default)s)')
parser.add_argument(
'--num_epochs', type=int, default=100,
help='Number of epochs to train for (default: %(default)s)')
parser.add_argument(
'--parallel', action='store_true', default=False,
help='Train with multiple GPUs (default: %(default)s)')
parser.add_argument(
'--G_fp16', action='store_true', default=False,
help='Train with half-precision in G? (default: %(default)s)')
parser.add_argument(
'--D_fp16', action='store_true', default=False,
help='Train with half-precision in D? (default: %(default)s)')
parser.add_argument(
'--D_mixed_precision', action='store_true', default=False,
help='Train with half-precision activations but fp32 params in D? '
'(default: %(default)s)')
parser.add_argument(
'--G_mixed_precision', action='store_true', default=False,
help='Train with half-precision activations but fp32 params in G? '
'(default: %(default)s)')
parser.add_argument(
'--accumulate_stats', action='store_true', default=False,
help='Accumulate "standing" batchnorm stats? (default: %(default)s)')
parser.add_argument(
'--num_standing_accumulations', type=int, default=16,
help='Number of forward passes to use in accumulating standing stats? '
'(default: %(default)s)')
### Bookkeping stuff ###
parser.add_argument(
'--G_eval_mode', action='store_true', default=False,
help='Run G in eval mode (running/standing stats?) at sample/test time? '
'(default: %(default)s)')
parser.add_argument(
'--save_every', type=int, default=2000,
help='Save every X iterations (default: %(default)s)')
parser.add_argument(
'--num_save_copies', type=int, default=2,
help='How many copies to save (default: %(default)s)')
parser.add_argument(
'--num_best_copies', type=int, default=2,
help='How many previous best checkpoints to save (default: %(default)s)')
parser.add_argument(
'--which_best', type=str, default='IS',
help='Which metric to use to determine when to save new "best"'
'checkpoints, one of IS or FID (default: %(default)s)')
parser.add_argument(
'--no_fid', action='store_true', default=False,
help='Calculate IS only, not FID? (default: %(default)s)')
parser.add_argument(
'--test_every', type=int, default=5000,
help='Test every X iterations (default: %(default)s)')
parser.add_argument(
'--num_inception_images', type=int, default=50000,
help='Number of samples to compute inception metrics with '
'(default: %(default)s)')
parser.add_argument(
'--hashname', action='store_true', default=False,
help='Use a hash of the experiment name instead of the full config '
'(default: %(default)s)')
parser.add_argument(
#'--base_root', type=str, default='/home/fk1/fahad/data/imagenet/single_cate/single_cate_imagenet/n01514668_hdf',
#'--base_root', type=str, default='/home/fk1/fahad/data/imagenet/single_cate/single_cate_places/arch',
#'--base_root', type=str, default='/home/fk1/fahad/data/imagenet/single_cate/single_cate_places/alley',
#'--base_root', type=str, default='/home/fk1/fahad/data/imagenet/single_cate/BigGAN_BSA_data/anime25_BSA',
#'--base_root', type=str, default='/home/fk1/fahad/data/imagenet/single_cate/single_cate_places/alley_hdf/alley100_hdf',
#'--base_root', type=str, default='/home/fk1/fahad/data/imagenet/single_cate/single_cate_places/alley_hdf/alley500_hdf',
'--base_root', type=str, default='/home/fk1/fahad/data/imagenet/single_cate/single_cate_places/alley_hdf/alley1000_hdf',
help='Default location to store all weights, samples, data, and logs '
' (default: %(default)s),/DATA/data/Biggan_pytorch/single_cate/n01440764_hdf, /DATA/data/Biggan_pytorch/good_original_data')
parser.add_argument(
'--data_root', type=str, default='',
help='Default location where data is stored (default: %(default)s)')
parser.add_argument(
'--weights_root', type=str, default='',
help='Default location to store weights (default: %(default)s)')
parser.add_argument(
'--logs_root', type=str, default='',
help='Default location to store logs (default: %(default)s)')
parser.add_argument(
'--samples_root', type=str, default='',
help='Default location to store samples (default: %(default)s)')
parser.add_argument(
'--pbar', type=str, default='mine',
help='Type of progressbar to use; one of "mine" or "tqdm" '
'(default: %(default)s)')
parser.add_argument(
'--name_suffix', type=str, default='',
help='Suffix for experiment name for loading weights for sampling '
'(consider "best0") (default: %(default)s)')
parser.add_argument(
'--experiment_name', type=str, default='',
help='Optionally override the automatic experiment naming with this arg. '
'(default: %(default)s)')
parser.add_argument(
'--config_from_name', action='store_true', default=False,
help='Use a hash of the experiment name instead of the full config '
'(default: %(default)s)')
### EMA Stuff ###
parser.add_argument(
'--ema', action='store_true', default=False,
help='Keep an ema of G''s weights? (default: %(default)s)')
parser.add_argument(
'--ema_decay', type=float, default=0.9999,
help='EMA decay rate (default: %(default)s)')
parser.add_argument(
'--use_ema', action='store_true', default=False,
help='Use the EMA parameters of G for evaluation? (default: %(default)s)')
parser.add_argument(
'--ema_start', type=int, default=0,
help='When to start updating the EMA weights (default: %(default)s)')
### Numerical precision and SV stuff ###
parser.add_argument(
'--adam_eps', type=float, default=1e-8,
help='epsilon value to use for Adam (default: %(default)s)')
parser.add_argument(
'--BN_eps', type=float, default=1e-5,
help='epsilon value to use for BatchNorm (default: %(default)s)')
parser.add_argument(
'--SN_eps', type=float, default=1e-8,
help='epsilon value to use for Spectral Norm(default: %(default)s)')
parser.add_argument(
'--num_G_SVs', type=int, default=1,
help='Number of SVs to track in G (default: %(default)s)')
parser.add_argument(
'--num_D_SVs', type=int, default=1,
help='Number of SVs to track in D (default: %(default)s)')
parser.add_argument(
'--num_G_SV_itrs', type=int, default=1,
help='Number of SV itrs in G (default: %(default)s)')
parser.add_argument(
'--num_D_SV_itrs', type=int, default=1,
help='Number of SV itrs in D (default: %(default)s)')
### Ortho reg stuff ###
parser.add_argument(
'--G_ortho', type=float, default=0.0, # 1e-4 is default for BigGAN
help='Modified ortho reg coefficient in G(default: %(default)s)')
parser.add_argument(
'--D_ortho', type=float, default=0.0,
help='Modified ortho reg coefficient in D (default: %(default)s)')
parser.add_argument(
'--toggle_grads', action='store_true', default=True,
help='Toggle D and G''s "requires_grad" settings when not training them? '
' (default: %(default)s)')
### Which train function ###
parser.add_argument(
'--which_train_fn', type=str, default='GAN',
help='How2trainyourbois (default: %(default)s)')
### Resume training stuff
parser.add_argument(
'--load_weights', type=str, default='',
help='Suffix for which weights to load (e.g. best0, copy0) '
'(default: %(default)s)')
parser.add_argument(
'--resume', action='store_true', default=False,
help='Resume training? (default: %(default)s)')
### Log stuff ###
parser.add_argument(
'--logstyle', type=str, default='%3.3e',
help='What style to use when logging training metrics?'
'One of: %#.#f/ %#.#e (float/exp, text),'
'pickle (python pickle),'
'npz (numpy zip),'
'mat (MATLAB .mat file) (default: %(default)s)')
parser.add_argument(
'--log_G_spectra', action='store_true', default=False,
help='Log the top 3 singular values in each SN layer in G? '
'(default: %(default)s)')
parser.add_argument(
'--log_D_spectra', action='store_true', default=False,
help='Log the top 3 singular values in each SN layer in D? '
'(default: %(default)s)')
parser.add_argument(
'--sv_log_interval', type=int, default=10,
help='Iteration interval for logging singular values '
' (default: %(default)s)')
return parser
# Arguments for sample.py; not presently used in train.py
def add_sample_parser(parser):
parser.add_argument(
'--sample_npz', action='store_true', default=False,
help='Sample "sample_num_npz" images and save to npz? '
'(default: %(default)s)')
parser.add_argument(
'--sample_num_npz', type=int, default=16,
help='Number of images to sample when sampling NPZs '
'(default: %(default)s)')
parser.add_argument(
'--sample_sheets', action='store_true', default=False,
help='Produce class-conditional sample sheets and stick them in '
'the samples root? (default: %(default)s)')
parser.add_argument(
'--sample_interps', action='store_true', default=False,
help='Produce interpolation sheets and stick them in '
'the samples root? (default: %(default)s)')
parser.add_argument(
'--sample_sheet_folder_num', type=int, default=-1,
help='Number to use for the folder for these sample sheets '
'(default: %(default)s)')
parser.add_argument(
'--sample_random', action='store_true', default=False,
help='Produce a single random sheet? (default: %(default)s)')
parser.add_argument(
'--sample_trunc_curves', type=str, default='',
help='Get inception metrics with a range of variances?'
'To use this, specify a startpoint, step, and endpoint, e.g. '
'--sample_trunc_curves 0.2_0.1_1.0 for a startpoint of 0.2, '
'endpoint of 1.0, and stepsize of 1.0. Note that this is '
'not exactly identical to using tf.truncated_normal, but should '
'have approximately the same effect. (default: %(default)s)')
parser.add_argument(
'--sample_inception_metrics', action='store_true', default=False,
help='Calculate Inception metrics with sample.py? (default: %(default)s)')
return parser
# Convenience dicts
dset_dict = {'I32': dset.ImageFolder, 'I64': dset.ImageFolder,
'I128': dset.ImageFolder, 'I256': dset.ImageFolder,
'I32_hdf5': dset.ILSVRC_HDF5, 'I64_hdf5': dset.ILSVRC_HDF5,
'I128_hdf5': dset.ILSVRC_HDF5, 'I256_hdf5': dset.ILSVRC_HDF5,
'C10': dset.CIFAR10, 'C100': dset.CIFAR100}
imsize_dict = {'I32': 32, 'I32_hdf5': 32,
'I64': 64, 'I64_hdf5': 64,
'I128': 128, 'I128_hdf5': 128,
'I256': 256, 'I256_hdf5': 256,
'C10': 32, 'C100': 32}
root_dict = {'I32': 'ImageNet', 'I32_hdf5': 'ILSVRC32.hdf5',
'I64': 'ImageNet', 'I64_hdf5': 'ILSVRC64.hdf5',
'I128': 'ImageNet', 'I128_hdf5': 'ILSVRC128.hdf5',
'I256': 'ImageNet', 'I256_hdf5': 'ILSVRC256.hdf5',
'C10': 'cifar', 'C100': 'cifar'}
nclass_dict = {'I32': 1000, 'I32_hdf5': 1000,
'I64': 1000, 'I64_hdf5': 1000,
'I128': 1000, 'I128_hdf5': 1000,
'I256': 1000, 'I256_hdf5': 1000,
'C10': 10, 'C100': 100}
# Number of classes to put per sample sheet
classes_per_sheet_dict = {'I32': 50, 'I32_hdf5': 50,
'I64': 50, 'I64_hdf5': 50,
'I128': 20, 'I128_hdf5': 20,
'I256': 20, 'I256_hdf5': 20,
'C10': 10, 'C100': 100}
activation_dict = {'inplace_relu': nn.ReLU(inplace=True),
'relu': nn.ReLU(inplace=False),
'ir': nn.ReLU(inplace=True),}
class CenterCropLongEdge(object):
"""Crops the given PIL Image on the long edge.
Args:
size (sequence or int): Desired output size of the crop. If size is an
int instead of sequence like (h, w), a square crop (size, size) is
made.
"""
def __call__(self, img):
"""
Args:
img (PIL Image): Image to be cropped.
Returns:
PIL Image: Cropped image.
"""
return transforms.functional.center_crop(img, min(img.size))
def __repr__(self):
return self.__class__.__name__
class RandomCropLongEdge(object):
"""Crops the given PIL Image on the long edge with a random start point.
Args:
size (sequence or int): Desired output size of the crop. If size is an
int instead of sequence like (h, w), a square crop (size, size) is
made.
"""
def __call__(self, img):
"""
Args:
img (PIL Image): Image to be cropped.
Returns:
PIL Image: Cropped image.
"""
size = (min(img.size), min(img.size))
# Only step forward along this edge if it's the long edge
i = (0 if size[0] == img.size[0]
else np.random.randint(low=0,high=img.size[0] - size[0]))
j = (0 if size[1] == img.size[1]
else np.random.randint(low=0,high=img.size[1] - size[1]))
return transforms.functional.crop(img, i, j, size[0], size[1])
def __repr__(self):
return self.__class__.__name__
# multi-epoch Dataset sampler to avoid memory leakage and enable resumption of
# training from the same sample regardless of if we stop mid-epoch
class MultiEpochSampler(torch.utils.data.Sampler):
r"""Samples elements randomly over multiple epochs
Arguments:
data_source (Dataset): dataset to sample from
num_epochs (int) : Number of times to loop over the dataset
start_itr (int) : which iteration to begin from
"""
def __init__(self, data_source, num_epochs, start_itr=0, batch_size=128):
self.data_source = data_source
self.num_samples = len(self.data_source)
self.num_epochs = num_epochs
self.start_itr = start_itr
self.batch_size = batch_size
if not isinstance(self.num_samples, int) or self.num_samples <= 0:
raise ValueError("num_samples should be a positive integeral "
"value, but got num_samples={}".format(self.num_samples))
def __iter__(self):
n = len(self.data_source)
# Determine number of epochs
num_epochs = int(np.ceil((n * self.num_epochs
- (self.start_itr * self.batch_size)) / float(n)))
# Sample all the indices, and then grab the last num_epochs index sets;
# This ensures if we're starting at epoch 4, we're still grabbing epoch 4's
# indices
out = [torch.randperm(n) for epoch in range(self.num_epochs)][-num_epochs:]
# Ignore the first start_itr % n indices of the first epoch
out[0] = out[0][(self.start_itr * self.batch_size % n):]
# if self.replacement:
# return iter(torch.randint(high=n, size=(self.num_samples,), dtype=torch.int64).tolist())
# return iter(.tolist())
output = torch.cat(out).tolist()
print('Length dataset output is %d' % len(output))
return iter(output)
def __len__(self):
return len(self.data_source) * self.num_epochs - self.start_itr * self.batch_size
# Convenience function to centralize all data loaders
def get_data_loaders(dataset, data_root=None, augment=False, batch_size=64,
num_workers=8, shuffle=True, load_in_mem=False, hdf5=False,
pin_memory=True, drop_last=True, start_itr=0,
num_epochs=500, use_multiepoch_sampler=False,
**kwargs):
# Append /FILENAME.hdf5 to root if using hdf5
data_root += '/%s' % root_dict[dataset]
print('Using dataset root location %s' % data_root)
which_dataset = dset_dict[dataset]
norm_mean = [0.5,0.5,0.5]
norm_std = [0.5,0.5,0.5]
image_size = imsize_dict[dataset]
# For image folder datasets, name of the file where we store the precomputed
# image locations to avoid having to walk the dirs every time we load.
dataset_kwargs = {'index_filename': '%s_imgs.npz' % dataset}
# HDF5 datasets have their own inbuilt transform, no need to train_transform
if 'hdf5' in dataset:
train_transform = None
else:
if augment:
print('Data will be augmented...')
if dataset in ['C10', 'C100']:
train_transform = [transforms.RandomCrop(32, padding=4),
transforms.RandomHorizontalFlip()]
else:
train_transform = [RandomCropLongEdge(),
transforms.Resize(image_size),
transforms.RandomHorizontalFlip()]
else:
print('Data will not be augmented...')
if dataset in ['C10', 'C100']:
train_transform = []
else:
train_transform = [CenterCropLongEdge(), transforms.Resize(image_size)]
# train_transform = [transforms.Resize(image_size), transforms.CenterCrop]
train_transform = transforms.Compose(train_transform + [
transforms.ToTensor(),
transforms.Normalize(norm_mean, norm_std)])
train_set = which_dataset(root=data_root, transform=train_transform,
load_in_mem=load_in_mem, **dataset_kwargs)
# Prepare loader; the loaders list is for forward compatibility with
# using validation / test splits.
loaders = []
start_itr = 0
if use_multiepoch_sampler:
print('Using multiepoch sampler from start_itr %d...' % start_itr)
loader_kwargs = {'num_workers': num_workers, 'pin_memory': pin_memory}
sampler = MultiEpochSampler(train_set, num_epochs, start_itr, batch_size)
train_loader = DataLoader(train_set, batch_size=batch_size,
sampler=sampler, **loader_kwargs)
else:
loader_kwargs = {'num_workers': num_workers, 'pin_memory': pin_memory,
'drop_last': drop_last} # Default, drop last incomplete batch
train_loader = DataLoader(train_set, batch_size=batch_size,
shuffle=shuffle, **loader_kwargs)
loaders.append(train_loader)
return loaders
# Utility file to seed rngs
def seed_rng(seed):
torch.manual_seed(seed)
torch.cuda.manual_seed(seed)
np.random.seed(seed)
# Utility to peg all roots to a base root
# If a base root folder is provided, peg all other root folders to it.
def update_config_roots(config):
if config['base_root']:
print('Pegging all root folders to base root %s' % config['base_root'])
for key in ['data', 'weights', 'logs', 'samples']:
config['%s_root' % key] = '%s/%s' % (config['base_root'], key)
return config
# Utility to prepare root folders if they don't exist; parent folder must exist
def prepare_root(config):
for key in ['weights_root', 'logs_root', 'samples_root']:
if not os.path.exists(config[key]):
print('Making directory %s for %s...' % (config[key], key))
#os.mkdir(config[key])
os.makedirs(config[key])
# Simple wrapper that applies EMA to a model. COuld be better done in 1.0 using
# the parameters() and buffers() module functions, but for now this works
# with state_dicts using .copy_
class ema(object):
def __init__(self, source, target, decay=0.9999, start_itr=0):
self.source = source
self.target = target
self.decay = decay
# Optional parameter indicating what iteration to start the decay at
self.start_itr = start_itr
# Initialize target's params to be source's
self.source_dict = self.source.state_dict()
self.target_dict = self.target.state_dict()
print('Initializing EMA parameters to be source parameters...')
with torch.no_grad():
for key in self.source_dict:
self.target_dict[key].data.copy_(self.source_dict[key].data)
# target_dict[key].data = source_dict[key].data # Doesn't work!
def update(self, itr=None):
# If an iteration counter is provided and itr is less than the start itr,
# peg the ema weights to the underlying weights.
if itr and itr < self.start_itr:
decay = 0.0
else:
decay = self.decay
with torch.no_grad():
for key in self.source_dict:
self.target_dict[key].data.copy_(self.target_dict[key].data * decay
+ self.source_dict[key].data * (1 - decay))
# Apply modified ortho reg to a model
# This function is an optimized version that directly computes the gradient,
# instead of computing and then differentiating the loss.
def ortho(model, strength=1e-4, blacklist=[]):
with torch.no_grad():
for param in model.parameters():
# Only apply this to parameters with at least 2 axes, and not in the blacklist
if len(param.shape) < 2 or any([param is item for item in blacklist]):
continue
w = param.view(param.shape[0], -1)
grad = (2 * torch.mm(torch.mm(w, w.t())
* (1. - torch.eye(w.shape[0], device=w.device)), w))
param.grad.data += strength * grad.view(param.shape)
# Default ortho reg
# This function is an optimized version that directly computes the gradient,
# instead of computing and then differentiating the loss.
def default_ortho(model, strength=1e-4, blacklist=[]):
with torch.no_grad():
for param in model.parameters():
# Only apply this to parameters with at least 2 axes & not in blacklist
if len(param.shape) < 2 or param in blacklist:
continue
w = param.view(param.shape[0], -1)
grad = (2 * torch.mm(torch.mm(w, w.t())
- torch.eye(w.shape[0], device=w.device), w))
param.grad.data += strength * grad.view(param.shape)
# Convenience utility to switch off requires_grad
def toggle_grad(model, on_or_off):
for param in model.parameters():
param.requires_grad = on_or_off
# Function to join strings or ignore them
# Base string is the string to link "strings," while strings
# is a list of strings or Nones.
def join_strings(base_string, strings):
return base_string.join([item for item in strings if item])
# Save a model's weights, optimizer, and the state_dict
def save_weights(G, D, M, state_dict, weights_root, experiment_name,
name_suffix=None, G_ema=None):
root = '/'.join([weights_root, experiment_name])
if not os.path.exists(root):
os.mkdir(root)
if name_suffix:
print('Saving weights to %s/%s...' % (root, name_suffix))
else:
print('Saving weights to %s...' % root)
torch.save(G.state_dict(),
'%s/%s.pth' % (root, join_strings('_', ['G', name_suffix])))
torch.save(G.optim.state_dict(),
'%s/%s.pth' % (root, join_strings('_', ['G_optim', name_suffix])))
torch.save(D.state_dict(),
'%s/%s.pth' % (root, join_strings('_', ['D', name_suffix])))
torch.save(D.optim.state_dict(),
'%s/%s.pth' % (root, join_strings('_', ['D_optim', name_suffix])))
torch.save(M.state_dict(),
'%s/%s.pth' % (root, join_strings('_', ['M', name_suffix])))
torch.save(M.optim.state_dict(),
'%s/%s.pth' % (root, join_strings('_', ['M_optim', name_suffix])))
torch.save(state_dict,
'%s/%s.pth' % (root, join_strings('_', ['state_dict', name_suffix])))
if G_ema is not None:
torch.save(G_ema.state_dict(),
'%s/%s.pth' % (root, join_strings('_', ['G_ema', name_suffix])))
# Load a model's weights, optimizer, and the state_dict
def load_weights(G, D, M, state_dict, weights_root, experiment_name,
name_suffix=None, G_ema=None, strict=True, load_optim=True):
#root = '/'.join([weights_root, experiment_name])
root = '/data/users/yaxing/MineGAN_I2I/BigGAN'
if name_suffix:
print('Loading %s weights from %s...' % (name_suffix, root))
else:
print('Loading weights from %s...' % root)
if G is not None:
G.load_state_dict(
torch.load('%s/%s.pth' % (root, join_strings('_', ['G', name_suffix]))),
strict=strict)
if load_optim:
G.optim.load_state_dict(
torch.load('%s/%s.pth' % (root, join_strings('_', ['G_optim', name_suffix]))))
if D is not None:
D.load_state_dict(
torch.load('%s/%s.pth' % (root, join_strings('_', ['D', name_suffix]))),
strict=strict)
if load_optim:
D.optim.load_state_dict(
torch.load('%s/%s.pth' % (root, join_strings('_', ['D_optim', name_suffix]))))
#if M is not None:
# M.load_state_dict(
# torch.load('%s/%s.pth' % (root, join_strings('_', ['M', name_suffix]))),
# strict=strict)
# if load_optim:
# M.optim.load_state_dict(
# torch.load('%s/%s.pth' % (root, join_strings('_', ['M_optim', name_suffix]))))
# Load state dict
for item in state_dict:
state_dict[item] = torch.load('%s/%s.pth' % (root, join_strings('_', ['state_dict', name_suffix])))[item]
if G_ema is not None:
G_ema.load_state_dict(
torch.load('%s/%s.pth' % (root, join_strings('_', ['G_ema', name_suffix]))),
strict=strict)
''' MetricsLogger originally stolen from VoxNet source code.
Used for logging inception metrics'''
class MetricsLogger(object):
def __init__(self, fname, reinitialize=False):
self.fname = fname
self.reinitialize = reinitialize
if os.path.exists(self.fname):
if self.reinitialize:
print('{} exists, deleting...'.format(self.fname))
os.remove(self.fname)
def log(self, record=None, **kwargs):
"""
Assumption: no newlines in the input.
"""
if record is None:
record = {}
record.update(kwargs)
record['_stamp'] = time.time()
with open(self.fname, 'a') as f:
f.write(json.dumps(record, ensure_ascii=True) + '\n')
# Logstyle is either:
# '%#.#f' for floating point representation in text
# '%#.#e' for exponent representation in text
# 'npz' for output to npz # NOT YET SUPPORTED
# 'pickle' for output to a python pickle # NOT YET SUPPORTED
# 'mat' for output to a MATLAB .mat file # NOT YET SUPPORTED
class MyLogger(object):
def __init__(self, fname, reinitialize=False, logstyle='%3.3f'):
self.root = fname
if not os.path.exists(self.root):
os.mkdir(self.root)
self.reinitialize = reinitialize
self.metrics = []
self.logstyle = logstyle # One of '%3.3f' or like '%3.3e'
# Delete log if re-starting and log already exists
def reinit(self, item):
if os.path.exists('%s/%s.log' % (self.root, item)):
if self.reinitialize:
# Only print the removal mess
if 'sv' in item :
if not any('sv' in item for item in self.metrics):
print('Deleting singular value logs...')
else:
print('{} exists, deleting...'.format('%s_%s.log' % (self.root, item)))
os.remove('%s/%s.log' % (self.root, item))
# Log in plaintext; this is designed for being read in MATLAB(sorry not sorry)
def log(self, itr, **kwargs):
for arg in kwargs:
if arg not in self.metrics:
if self.reinitialize:
self.reinit(arg)
self.metrics += [arg]
if self.logstyle == 'pickle':
print('Pickle not currently supported...')
# with open('%s/%s.log' % (self.root, arg), 'a') as f:
# pickle.dump(kwargs[arg], f)
elif self.logstyle == 'mat':
print('.mat logstyle not currently supported...')
else:
with open('%s/%s.log' % (self.root, arg), 'a') as f:
f.write('%d: %s\n' % (itr, self.logstyle % kwargs[arg]))
# Write some metadata to the logs directory
def write_metadata(logs_root, experiment_name, config, state_dict):
with open(('%s/%s/metalog.txt' %
(logs_root, experiment_name)), 'w') as writefile:
writefile.write('datetime: %s\n' % str(datetime.datetime.now()))
writefile.write('config: %s\n' % str(config))
writefile.write('state: %s\n' %str(state_dict))
"""
Very basic progress indicator to wrap an iterable in.
Author: Jan Schlüter
Andy's adds: time elapsed in addition to ETA, makes it possible to add
estimated time to 1k iters instead of estimated time to completion.
"""
def progress(items, desc='', total=None, min_delay=0.1, displaytype='s1k'):
"""
Returns a generator over `items`, printing the number and percentage of
items processed and the estimated remaining processing time before yielding
the next item. `total` gives the total number of items (required if `items`
has no length), and `min_delay` gives the minimum time in seconds between
subsequent prints. `desc` gives an optional prefix text (end with a space).
"""
total = total or len(items)
t_start = time.time()
t_last = 0
for n, item in enumerate(items):
t_now = time.time()
if t_now - t_last > min_delay:
print("\r%s%d/%d (%6.2f%%)" % (
desc, n+1, total, n / float(total) * 100), end=" ")
if n > 0:
if displaytype == 's1k': # minutes/seconds for 1000 iters
next_1000 = n + (1000 - n%1000)
t_done = t_now - t_start
t_1k = t_done / n * next_1000
outlist = list(divmod(t_done, 60)) + list(divmod(t_1k - t_done, 60))
print("(TE/ET1k: %d:%02d / %d:%02d)" % tuple(outlist), end=" ")
else:# displaytype == 'eta':
t_done = t_now - t_start
t_total = t_done / n * total
outlist = list(divmod(t_done, 60)) + list(divmod(t_total - t_done, 60))
print("(TE/ETA: %d:%02d / %d:%02d)" % tuple(outlist), end=" ")
sys.stdout.flush()
t_last = t_now
yield item
t_total = time.time() - t_start
print("\r%s%d/%d (100.00%%) (took %d:%02d)" % ((desc, total, total) +
divmod(t_total, 60)))
# Sample function for use with inception metrics
def sample(G, M, z_, y_, config):
with torch.no_grad():
z_.sample_()
y_.sample_()
if config['parallel']:
#z_from_M, y_from_M = M(z_)
z_from_M, y_from_M = nn.parallel.data_parallel(M, (z_,))
G_z = nn.parallel.data_parallel(G, (z_from_M, y_from_M))
#G_z = nn.parallel.data_parallel(G, (z_, G.shared(y_)))
else:
# yaxing
z_from_M, y_from_M = M(z_)
G_z = G(z_from_M, y_from_M)
#G_z = G(z_, G.shared(y_))
return G_z, y_
# Sample function for sample sheets
def sample_sheet(G,M, classes_per_sheet, num_classes, samples_per_class, parallel,
samples_root, experiment_name, folder_number, z_=None):
# Prepare sample directory
if not os.path.isdir('%s/%s' % (samples_root, experiment_name)):
os.mkdir('%s/%s' % (samples_root, experiment_name))
if not os.path.isdir('%s/%s/%d' % (samples_root, experiment_name, folder_number)):
os.mkdir('%s/%s/%d' % (samples_root, experiment_name, folder_number))
# loop over total number of sheets
for i in range(num_classes // classes_per_sheet):
ims = []
y = torch.arange(i * classes_per_sheet, (i + 1) * classes_per_sheet, device='cuda')
y = torch.ones_like(y)*0
for j in range(samples_per_class):
if (z_ is not None) and hasattr(z_, 'sample_') and classes_per_sheet <= z_.size(0):
z_.sample_()
else:
z_ = torch.randn(classes_per_sheet, G.dim_z, device='cuda')
with torch.no_grad():
if parallel:
z_from_M, y_from_M = nn.parallel.data_parallel(M, (z_[:classes_per_sheet],))
o = nn.parallel.data_parallel(G, (z_from_M, y_from_M))
#o = nn.parallel.data_parallel(G, (z_[:classes_per_sheet], G.shared(y)))
else:
z_from_M, y_from_M = M(z_[:classes_per_sheet])
o = G(z_from_M, y_from_M)
#o = G(z_[:classes_per_sheet], G.shared(y))
ims += [o.data.cpu()]
# This line should properly unroll the images
out_ims = torch.stack(ims, 1).view(-1, ims[0].shape[1], ims[0].shape[2],
ims[0].shape[3]).data.float().cpu()
# The path for the samples
image_filename = '%s/%s/%d/samples%d.jpg' % (samples_root, experiment_name,
folder_number, i)
torchvision.utils.save_image(out_ims, image_filename,
nrow=samples_per_class, normalize=True)
# Interp function; expects x0 and x1 to be of shape (shape0, 1, rest_of_shape..)
def interp(x0, x1, num_midpoints):
lerp = torch.linspace(0, 1.0, num_midpoints + 2, device='cuda').to(x0.dtype)
return ((x0 * (1 - lerp.view(1, -1, 1))) + (x1 * lerp.view(1, -1, 1)))
# interp sheet function
# Supports full, class-wise and intra-class interpolation
def interp_sheet(G, num_per_sheet, num_midpoints, num_classes, parallel,
samples_root, experiment_name, folder_number, sheet_number=0,
fix_z=False, fix_y=False, device='cuda'):
# Prepare zs and ys
if fix_z: # If fix Z, only sample 1 z per row
zs = torch.randn(num_per_sheet, 1, G.dim_z, device=device)
zs = zs.repeat(1, num_midpoints + 2, 1).view(-1, G.dim_z)
else:
zs = interp(torch.randn(num_per_sheet, 1, G.dim_z, device=device),
torch.randn(num_per_sheet, 1, G.dim_z, device=device),
num_midpoints).view(-1, G.dim_z)
if fix_y: # If fix y, only sample 1 z per row
ys = sample_1hot(num_per_sheet, num_classes)
ys = G.shared(ys).view(num_per_sheet, 1, -1)
ys = ys.repeat(1, num_midpoints + 2, 1).view(num_per_sheet * (num_midpoints + 2), -1)
else:
ys = interp(G.shared(sample_1hot(num_per_sheet, num_classes)).view(num_per_sheet, 1, -1),
G.shared(sample_1hot(num_per_sheet, num_classes)).view(num_per_sheet, 1, -1),
num_midpoints).view(num_per_sheet * (num_midpoints + 2), -1)
# Run the net--note that we've already passed y through G.shared.
if G.fp16:
zs = zs.half()
with torch.no_grad():
if parallel:
out_ims = nn.parallel.data_parallel(G, (zs, ys)).data.cpu()
else:
out_ims = G(zs, ys).data.cpu()
interp_style = '' + ('Z' if not fix_z else '') + ('Y' if not fix_y else '')
image_filename = '%s/%s/%d/interp%s%d.jpg' % (samples_root, experiment_name,
folder_number, interp_style,
sheet_number)
torchvision.utils.save_image(out_ims, image_filename,
nrow=num_midpoints + 2, normalize=True)
# Convenience debugging function to print out gradnorms and shape from each layer
# May need to rewrite this so we can actually see which parameter is which
def print_grad_norms(net):
gradsums = [[float(torch.norm(param.grad).item()),
float(torch.norm(param).item()), param.shape]
for param in net.parameters()]
order = np.argsort([item[0] for item in gradsums])
print(['%3.3e,%3.3e, %s' % (gradsums[item_index][0],
gradsums[item_index][1],
str(gradsums[item_index][2]))
for item_index in order])
# Get singular values to log. This will use the state dict to find them
# and substitute underscores for dots.
def get_SVs(net, prefix):