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sample_IAN.py
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sample_IAN.py
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import argparse
import imp
import time
import logging
import itertools
import os
import numpy as np
from path import Path
import theano
import theano.tensor as T
from theano.tensor.opt import register_canonicalize
from theano.sandbox.rng_mrg import MRG_RandomStreams as RandomStreams
import lasagne
from lasagne.layers import SliceLayer as SL
import GANcheckpoints
from collections import OrderedDict
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
from fuel.datasets import CelebA
from discgen_utils import plot_image_grid
## Utilities:
# to_tanh: transforms an array in the range [0,255] to the range [-1,1]
# from_tanh: transforms an array in the range [-1,1] to the range[0,255]
def to_tanh(input):
return 2.0*(input/255.0)-1.0
# return input/255.0
def from_tanh(input):
return 255.0*(input+1)/2.0
# return 255.0*input
### Make Training Functions Method
# This function defines and compiles the computational graphs that define the training, validation, and test functions.
def make_training_functions(cfg,model):
# Define input tensors
# Tensor axes are batch-channel-dim1-dim2
# Image Input
X = T.TensorType('float32', [False]*4)('X')
# Latent Input, for providing latent values from the main function
Z = T.TensorType('float32', [False]*2)('Z') # Latents
# Input layer
l_in = model['l_in']
# Output layer
l_out = model['l_out']
# Latent Layer
l_Z = model['l_Z']
# IAF latent layer:
l_Z_IAF = model['l_Z_IAF']
# Means
l_mu = model['l_mu']
# Log-sigmas
l_ls = model['l_ls']
# IAF Means
l_IAF_mu = model['l_IAF_mu']
# IAF logsigmas
l_IAF_ls = model['l_IAF_ls']
# Introspective loss layers
l_introspect = model['l_introspect']
# Adversarial Discriminator
l_discrim = model['l_discrim']
# Sample function
sample = theano.function([Z],lasagne.layers.get_output(l_out,{l_Z_IAF:Z},deterministic=True),on_unused_input='warn')
sampleZ= theano.function([Z],lasagne.layers.get_output(l_out,{l_Z:Z},deterministic=True),on_unused_input='warn')
# Inference Function--Infer non-IAF_latents given an input X
Zfn = theano.function([X],lasagne.layers.get_output(l_Z_IAF,{l_in:X},deterministic=True),on_unused_input='warn')
# IAF function--Infer IAF latents given a latent input Z
Z_IAF_fn = theano.function([Z],lasagne.layers.get_output(l_Z,{l_Z_IAF:Z},deterministic=True),on_unused_input='warn')
# Dictionary of Theano Functions
# tfuncs = {'update_iter':update_iter,
tfuncs = {'sample': sample,
'sampleZ': sampleZ,
'Zfn' : Zfn,
'Z_IAF_fn': Z_IAF_fn
}
# Dictionary of Theano Variables
tvars = {'X' : X,
'Z' : Z}
return tfuncs, tvars, model
# Data Loading Function
#
# This function interfaces with a Fuel dataset and returns numpy arrays containing the requested data
def data_loader(cfg,set,offset=0,shuffle=False,seed=42):
# Define chunk size
chunk_size = cfg['batch_size']*cfg['batches_per_chunk']
np.random.seed(seed)
index = np.random.permutation(set.num_examples-offset) if shuffle else np.asarray(range(set.num_examples-offset))
# Open Dataset
set.open()
# Loop across all data
for i in xrange(set.num_examples//chunk_size):
yield to_tanh(np.float32(set.get_data(request = list(index[range(offset+chunk_size*i,offset+chunk_size*(i+1))]))[0]))
# Close dataset
set.close(state=None)
# Main Function
def main(args):
# Load Config Module from source file
config_module = imp.load_source('config', args.config_path)
# Get configuration parameters
cfg = config_module.cfg
# Define name of npz file to which the model parameters will be saved
weights_fname = str(args.config_path)[:-3]+'.npz'
model = config_module.get_model(interp=False)
print('Compiling theano functions...')
# Compile functions
tfuncs, tvars,model = make_training_functions(cfg,model)
# Test set for interpolations
test_set = CelebA('64',('test',),sources=('features',))
# Loop across epochs
offset = True
params = list(set(lasagne.layers.get_all_params(model['l_out'],trainable=True)+\
lasagne.layers.get_all_params(model['l_discrim'],trainable=True)+\
[x for x in lasagne.layers.get_all_params(model['l_out'])+\
lasagne.layers.get_all_params(model['l_discrim']) if x.name[-4:]=='mean' or x.name[-7:]=='inv_std']))
metadata = GANcheckpoints.load_weights(weights_fname, params)
epoch = args.epoch if args.epoch>0 else metadata['epoch'] if 'epoch' in metadata else 0
print('loading weights, epoch is '+str(epoch))
model['l_IAF_mu'].reset("Once")
model['l_IAF_ls'].reset("Once")
# Open Test Set
test_set.open()
np.random.seed(epoch*42+5)
# Generate Random Samples, averaging latent vectors across masks
samples = np.uint8(from_tanh(tfuncs['sample'](np.random.randn(27,cfg['num_latents']).astype(np.float32))))
np.random.seed(epoch*42+5)
# Get Reconstruction/Interpolation Endpoints
endpoints = np.uint8(test_set.get_data(request = list(np.random.choice(test_set.num_examples,6,replace=False)))[0])
# Get reconstruction latents
Ze = np.asarray(tfuncs['Zfn'](to_tanh(np.float32(endpoints))))
# Get Interpolant Latents
Z = np.asarray([Ze[2 * i, :] * (1 - j) + Ze[2 * i + 1, :] * j for i in range(3) for j in [x/6.0 for x in range(7)]],dtype=np.float32)
# Get all images
images = np.append(samples,np.concatenate([np.insert(endpoints[2*i:2*(i+1),:,:,:],1,np.uint8(from_tanh(tfuncs['sample'](Z[7*i:7*(i+1),:]))),axis=0) for i in range(3)],axis=0),axis=0)
# Plot images
plot_image_grid(images,6,9,'pics/'+str(args.config_path)[:-3]+'_sample'+str(epoch)+'.png')
# Close test set
test_set.close(state=None)
if __name__=='__main__':
parser = argparse.ArgumentParser()
parser.add_argument('config_path', type=Path, help='config .py file')
parser.add_argument('--epoch',type=int,default=0)
args = parser.parse_args()
main(args)