Improve docs, update scripts

calculate_inception_moments.py

@@ -1,6 +1,11 @@
-# This script iterates over the dataset and calculates the moments of the 
-# activations of the Inception net (needed for FID), and also returns
-# the Inception Score of the training data.
+''' Calculate Inception Moments
+ This script iterates over the dataset and calculates the moments of the 
+ activations of the Inception net (needed for FID), and also returns
+ the Inception Score of the training data.
+ 
+ Note that if you don't shuffle the data, the IS of true data will be under-
+ estimated as it is label-ordered. By default, the data is not shuffled
+ so as to reduce non-determinism. '''
 import numpy as np
 import torch
 import torch.nn as nn
@@ -15,11 +20,11 @@ def prepare_parser():
   usage = 'Calculate and store inception metrics.'
   parser = ArgumentParser(description=usage)
   parser.add_argument(
-    '--dataset', type=str, default='I128',
+    '--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 of the dataset. (default: %(default)s)')
   parser.add_argument(
-    '--dataset_root', type=str, default='/home/s1580274/scratch/data/',
+    '--dataset_root', type=str, default='data',
     help='Default location where data is stored (default: %(default)s)') 
   parser.add_argument(
     '--batch_size', type=int, default=64,
@@ -67,7 +72,8 @@ def run(config):
   print('Calculating inception metrics...')
   IS_mean, IS_std = inception_utils.calculate_inception_score(logits)
   print('Training data from dataset %s has IS of %5.5f +/- %5.5f' % (config['dataset'], IS_mean, IS_std))
-  # Prepare mu and sigma, save to disk
+  # Prepare mu and sigma, save to disk. Remove "hdf5" by default 
+  # (the FID code also knows to strip "hdf5")
   print('Calculating means and covariances...')
   mu, sigma = np.mean(pool, axis=0), np.cov(pool, rowvar=False)
   print('Saving calculated means and covariances to disk...')

datasets.py

@@ -1,3 +1,6 @@
+''' Datasets
+    This file contains definitions for our CIFAR, ImageFolder, and HDF5 datasets
+'''
 import os
 import os.path
 import sys
@@ -10,8 +13,7 @@
 from torchvision.datasets.utils import download_url, check_integrity
 import torch.utils.data as data
 from torch.utils.data import DataLoader
-
-# Stuff for full imagenet            
+
 IMG_EXTENSIONS = ['.jpg', '.jpeg', '.png', '.ppm', '.bmp', '.pgm']
 
 
@@ -106,9 +108,12 @@ def __init__(self, root, transform=None, target_transform=None,
                loader=default_loader, load_in_mem=False, 
                index_filename='imagenet_imgs.npz', **kwargs):
     classes, class_to_idx = find_classes(root)
+    # Load pre-computed image directory walk
     if os.path.exists(index_filename):
       print('Loading pre-saved Index file %s...' % index_filename)
       imgs = np.load(index_filename)['imgs']
+    # If first time, walk the folder directory and save the 
+    # results to a pre-computed file.
     else:
       print('Generating  Index file %s...' % index_filename)
       imgs = make_dataset(root, class_to_idx)
@@ -171,19 +176,9 @@ def __repr__(self):
     fmt_str += '{0}{1}'.format(tmp, self.target_transform.__repr__().replace('\n', '\n' + ' ' * len(tmp)))
     return fmt_str
 
-
-# class ImageNetA(ImageFolder):
-    # def __init__(self, root, transform=None, target_transform=None,
-                 # loader=default_loader, load_in_mem=False,
-                 # train=True,download=False, validate_seed=0,
-                 # val_split=0):
-        # super(ImageNetA, self).__init__(root, transform, target_transform,
-                 # default_loader, load_in_mem, train, download, validate_seed,
-                 # val_split):
-
-
-# Imagenet at 256 with '/home/s1580274/scratch/ILSVRC256.hdf5'
 
+''' ILSVRC_HDF5: A dataset to support I/O from an HDF5 to avoid
+    having to load individual images all the time. '''
 import h5py as h5
 import torch
 class ILSVRC_HDF5(data.Dataset):
@@ -250,7 +245,7 @@ class CIFAR10(dset.CIFAR10):
   def __init__(self, root, train=True,
            transform=None, target_transform=None,
            download=True, validate_seed=0,
-           val_split=0, load_in_mem=True):
+           val_split=0, load_in_mem=True, **kwargs):
     self.root = os.path.expanduser(root)
     self.transform = transform
     self.target_transform = target_transform
@@ -264,9 +259,7 @@ def __init__(self, root, train=True,
       raise RuntimeError('Dataset not found or corrupted.' +
                            ' You can use download=True to download it')
 
-    # now load the picked numpy arrays
-
-
+    # now load the picked numpy arrays    
     self.data = []
     self.labels= []
     for fentry in self.train_list:
@@ -294,15 +287,10 @@ def __init__(self, root, train=True,
 
       # randomly grab 500 elements of each class
       np.random.seed(validate_seed)
-
       self.val_indices = []           
-
-
-
       for l_i in label_indices:
         self.val_indices += list(l_i[np.random.choice(len(l_i), int(len(self.data) * val_split) // (max(self.labels) + 1) ,replace=False)])
-
-
+
     if self.train=='validate':    
       self.data = self.data[self.val_indices]
       self.labels = list(np.asarray(self.labels)[self.val_indices])

inception_tf13.py

@@ -1,7 +1,11 @@
-## Tensorflow inception score code
-# Derived from https://github.com/openai/improved-gan
-# Code derived from tensorflow/tensorflow/models/image/imagenet/classify_image.py
-# THIS CODE REQUIRES TENSORFLOW 1.3 or EARLIER to run in BATCH MODE
+''' Tensorflow inception score code
+Derived from https://github.com/openai/improved-gan
+Code derived from tensorflow/tensorflow/models/image/imagenet/classify_image.py
+THIS CODE REQUIRES TENSORFLOW 1.3 or EARLIER to run in PARALLEL BATCH MODE 
+
+To use this code, run sample.py on your model with --sample_npz, and then 
+pass the experiment name in the --experiment_name 
+'''
 from __future__ import absolute_import
 from __future__ import division
 from __future__ import print_function
@@ -17,20 +21,18 @@
 from six.moves import urllib
 import tensorflow as tf
 
-MODEL_DIR = '/home/s1580274/scratch'
+MODEL_DIR = ''
 DATA_URL = 'http://download.tensorflow.org/models/image/imagenet/inception-2015-12-05.tgz'
 softmax = None
 
-# Run on eddie with /home/s1580274/group/myconda/tensorflow/bin/python
-#fname='BigGAN_I128_hdf5_seed0_Gch64_Dch64_bs128_nDa4_nGa4_Glr1.0e-04_Dlr4.0e-04_Gnlrelu_Dnlrelu_Ginitxavier_Dinitxavier_Gattn64_Dattn64_Gshared_ema_SAGAN_bs128x4_ema'
 def prepare_parser():
   usage = 'Parser for TF1.3- Inception Score scripts.'
   parser = ArgumentParser(description=usage)
   parser.add_argument(
     '--experiment_name', type=str, default='',
     help='Which experiment''s samples.npz file to pull and evaluate')
   parser.add_argument(
-    '--experiment_root', type=str, default='/home/s1580274/scratch/samples/',
+    '--experiment_root', type=str, default='samples',
     help='Default location where samples are stored (default: %(default)s)')
   parser.add_argument(
     '--batch_size', type=int, default=500,
@@ -110,13 +112,12 @@ def _progress(count, block_size, total_size):
       logits = tf.matmul(tf.squeeze(pool3), w)
       softmax = tf.nn.softmax(logits)
 
-  # if softmax is None:
+  # if softmax is None: # No need to functionalize like this.
   _init_inception()
 
   fname = '%s/%s/samples.npz' % (config['experiment_root'], config['experiment_name'])
   print('loading %s ...'%fname)
-  ims = np.load(fname)['x']# + '_samples.npz')['x']
-  # ims = 
+  ims = np.load(fname)['x']
   import time
   t0 = time.time()
   inc = get_inception_score(list(ims.swapaxes(1,2).swapaxes(2,3)), splits=10)

inception_utils.py

@@ -1,5 +1,18 @@
+''' Inception utilities
+    This file contains methods for calculating IS and FID, using either
+    the original numpy code or an accelerated fully-pytorch version that 
+    uses a fast newton-schulz approximation for the matrix sqrt. There are also
+    methods for acquiring a desired number of samples from the Generator,
+    and parallelizing the inbuilt PyTorch inception network.
+    
+    NOTE that Inception Scores and FIDs calculated using these methods will 
+    *not* be directly comparable to values calculated using the original TF
+    IS/FID code. You *must* use the TF model if you wish to report and compare
+    numbers. This code tends to produce IS values that are 5-10% lower than
+    those obtained through TF. 
+'''    
 import numpy as np
-from scipy import linalg # For FID
+from scipy import linalg # For numpy FID
 import time
 
 import torch
@@ -8,6 +21,7 @@
 from torch.nn import Parameter as P
 from torchvision.models.inception import inception_v3
 
+
 # Module that wraps the inception network to enable use with dataparallel and
 # returning pool features and logits.
 class WrapInception(nn.Module):
@@ -69,6 +83,7 @@ def forward(self, x):
     # 1000 (num_classes)
     return pool, logits
 
+
 # A pytorch implementation of cov, from Modar M. Alfadly
 # https://discuss.pytorch.org/t/covariance-and-gradient-support/16217/2
 def torch_cov(m, rowvar=False):
@@ -103,9 +118,9 @@ def torch_cov(m, rowvar=False):
     mt = m.t()  # if complex: mt = m.t().conj()
     return fact * m.matmul(mt).squeeze()
 
+
 # Pytorch implementation of matrix sqrt, from Tsung-Yu Lin, and Subhransu Maji
 # https://github.com/msubhransu/matrix-sqrt 
-
 def sqrt_newton_schulz(A, numIters, dtype=None):
   with torch.no_grad():
     if dtype is None:
@@ -123,9 +138,9 @@ def sqrt_newton_schulz(A, numIters, dtype=None):
     sA = Y*torch.sqrt(normA).view(batchSize, 1, 1).expand_as(A)
   return sA
 
+
 # FID calculator from TTUR--consider replacing this with GPU-accelerated cov
 # calculations using torch?
-
 def numpy_calculate_frechet_distance(mu1, sigma1, mu2, sigma2, eps=1e-6):
   """Numpy implementation of the Frechet Distance.
   Taken from https://github.com/bioinf-jku/TTUR
@@ -180,7 +195,7 @@ def numpy_calculate_frechet_distance(mu1, sigma1, mu2, sigma2, eps=1e-6):
 
   out = diff.dot(diff) + np.trace(sigma1) + np.trace(sigma2) - 2 * tr_covmean
   return out
-  # # return () +
+
 
 def torch_calculate_frechet_distance(mu1, sigma1, mu2, sigma2, eps=1e-6):
   """Pytorch implementation of the Frechet Distance.
@@ -214,6 +229,8 @@ def torch_calculate_frechet_distance(mu1, sigma1, mu2, sigma2, eps=1e-6):
   out = (diff.dot(diff) +  torch.trace(sigma1) + torch.trace(sigma2)
          - 2 * torch.trace(covmean))
   return out
+
+
 # Calculate Inception Score mean + std given softmax'd logits and number of splits
 def calculate_inception_score(pred, num_splits=10):
   scores = []

layers.py

@@ -1,3 +1,6 @@
+''' Layers
+    This file contains various layers for the BigGAN models.
+'''
 import numpy as np
 import torch
 import torch.nn as nn
@@ -8,6 +11,7 @@
 
 from sync_batchnorm import SynchronizedBatchNorm2d as SyncBN2d
 
+
 # Projection of x onto y
 def proj(x, y):
   return torch.mm(y, x.t()) * y / torch.mm(y, y.t())
@@ -45,11 +49,13 @@ def power_iteration(W, u_, update=True, eps=1e-12):
     #svs += [torch.sum(F.linear(u, W.transpose(0, 1)) * v)]
   return svs, us, vs
 
+
 # Convenience passthrough function
 class identity(nn.Module):
   def forward(self, input):
     return input
 
+
 # Spectral normalization base class 
 class SN(object):
   def __init__(self, num_svs, num_itrs, num_outputs, transpose=False, eps=1e-12):
@@ -105,6 +111,7 @@ def forward(self, x):
     return F.conv2d(x, self.W_(), self.bias, self.stride, 
                     self.padding, self.dilation, self.groups)
 
+
 # Linear layer with spectral norm
 class SNLinear(nn.Linear, SN):
   def __init__(self, in_features, out_features, bias=True,
@@ -114,6 +121,7 @@ def __init__(self, in_features, out_features, bias=True,
   def forward(self, x):
     return F.linear(x, self.W_(), self.bias)
 
+
 # Embedding layer with spectral norm
 # We use num_embeddings as the dim instead of embedding_dim here
 # for convenience sake
@@ -319,7 +327,8 @@ def extra_repr(self):
     s = 'out: {output_size}, in: {input_size},'
     s +=' cross_replica={cross_replica}'
     return s.format(**self.__dict__)
-
+
+
 # Normal, non-class-conditional BN
 class bn(nn.Module):
   def __init__(self, output_size,  eps=1e-5, momentum=0.1,
@@ -355,15 +364,14 @@ def forward(self, x, y=None):
     else:
       return F.batch_norm(x, self.stored_mean, self.stored_var, self.gain,
                           self.bias, self.training, self.momentum, self.eps)
-
+
+
 # Generator blocks
 # Note that this class assumes the kernel size and padding (and any other
 # settings) have been selected in the main generator module and passed in
 # through the which_conv arg. Similar rules apply with which_bn (the input
 # size [which is actually the number of channels of the conditional info] must 
 # be preselected)
-""" Andy's note: I changed activation to NONE to enforce passing in an activation
-"""
 class GBlock(nn.Module):
   def __init__(self, in_channels, out_channels,
                which_conv=nn.Conv2d, which_bn=bn, activation=None, 
@@ -401,8 +409,6 @@ def forward(self, x, y):
 
 
 # Residual block for the discriminator
-""" Andy's note: I changed activation to NONE to enforce passing in an activation
-"""
 class DBlock(nn.Module):
   def __init__(self, in_channels, out_channels, which_conv=SNConv2d, wide=True,
                preactivation=False, activation=None, downsample=None,):