def populateFeedGraph(self, shuffle=True, cutPaperOut=False):
with tf.name_scope("load_images"):
#Create a tensor out of the list of paths
filenamesTensor = tf.constant(self.pathList)
#Reads a slice of the tensor, for example, if the tensor is of shape [100,2], the slice shape should be [2] (to check if we have problem here)
dataset = tf.data.Dataset.from_tensor_slices(filenamesTensor)
#for each slice apply the __readImages function
dataset = dataset.map(self.__readImages,
num_parallel_calls=int(
multiprocessing.cpu_count() / 4))
#Authorize repetition of the dataset when one epoch is over.
if shuffle:
dataset = dataset.shuffle(buffer_size=16,
reshuffle_each_iteration=True)
#set batch size
dataset = dataset.repeat()
batched_dataset = dataset.batch(self.batchSize)
batched_dataset = batched_dataset.prefetch(buffer_size=4)
#Create an iterator to be initialized
iterator = batched_dataset.make_initializable_iterator()
#Create the node to retrieve next batch
paths_batch, inputs_batch, targets_batch, gammadInputBatch = iterator.get_next(
)
self.gammaCorrectedInputsBatch = gammadInputBatch
reshaped_targets = helpers.target_reshape(targets_batch)
#inputRealSize = self.tileSize
inputRealSize = self.inputImageSize
#Do the random crop, if the crop if fix, crop in the middle
if inputRealSize > self.tileSize:
if self.fixCrop:
xyCropping = (inputRealSize - self.tileSize) // 2
xyCropping = [xyCropping, xyCropping]
else:
xyCropping = tf.random_uniform([1],
0,
inputRealSize -
self.tileSize,
dtype=tf.int32)
inputs_batch = inputs_batch[:, :, xyCropping[0]:xyCropping[0] +
self.tileSize,
xyCropping[0]:xyCropping[0] +
self.tileSize, :]
targets_batch = targets_batch[:, :,
xyCropping[0]:xyCropping[0] +
self.tileSize,
xyCropping[0]:xyCropping[0] +
self.tileSize, :]
#Set shapes
inputs_batch.set_shape([None, self.tileSize, self.tileSize, 3])
targets_batch.set_shape(
[None, self.nbTargetsToRead, self.tileSize, self.tileSize, 3])
#Populate the object
self.stepsPerEpoch = int(
math.floor(len(self.pathList) / self.batchSize))
self.inputBatch = inputs_batch
self.targetBatch = targets_batch
self.iterator = iterator
self.pathBatch = paths_batch
def __renderInputs(self, materials, renderingScene, jitterLightPos,
jitterViewPos, mixMaterials):
fullSizeMixedMaterial = materials
if mixMaterials:
alpha = tf.random_uniform([1],
minval=0.1,
maxval=0.9,
dtype=tf.float32,
name="mixAlpha")
materials1 = materials[::2]
materials2 = materials[1::2]
fullSizeMixedMaterial = helpers.mixMaterials(
materials1, materials2, alpha)
if self.inputImageSize >= self.tileSize:
if self.fixCrop:
xyCropping = (self.inputImageSize - self.tileSize) // 2
xyCropping = [xyCropping, xyCropping]
else:
xyCropping = tf.random_uniform([2],
0,
self.inputImageSize -
self.tileSize,
dtype=tf.int32)
cropped_mixedMaterial = fullSizeMixedMaterial[:, :, xyCropping[
0]:xyCropping[0] + self.tileSize, xyCropping[1]:xyCropping[1] +
self.tileSize, :]
elif self.inputImageSize < self.tileSize:
raise Exception(
"Size of the input is inferior to the size of the rendering, please provide higher resolution maps"
)
cropped_mixedMaterial.set_shape(
[None, self.nbTargetsToRead, self.tileSize, self.tileSize, 3])
mixedMaterial = helpers.adaptRougness(cropped_mixedMaterial)
targetstoRender = helpers.target_reshape(
mixedMaterial
) #reshape it to be compatible with the rendering algorithm [?, size, size, 12]
nbRenderings = 1
rendererInstance = renderer.GGXRenderer(includeDiffuse=True)
## Do renderings of the mixedMaterial
targetstoRender = helpers.preprocess(
targetstoRender) #Put targets to -1; 1
surfaceArray = helpers.generateSurfaceArray(self.tileSize)
inputs = helpers.generateInputRenderings(
rendererInstance,
targetstoRender,
self.batchSize,
nbRenderings,
surfaceArray,
renderingScene,
jitterLightPos,
jitterViewPos,
self.useAmbientLight,
useAugmentationInRenderings=self.useAugmentationInRenderings)
self.gammaCorrectedInputsBatch = tf.squeeze(inputs, [1])
inputs = tf.pow(inputs, 2.2) # correct gamma
if self.logInput:
inputs = helpers.logTensor(inputs)
inputs = helpers.preprocess(inputs) #Put inputs to -1; 1
targets = helpers.target_deshape(targetstoRender, self.nbTargetsToRead)
return targets, inputs
def main():
if a.seed is None:
a.seed = random.randint(0, 2**31 - 1)
tf.set_random_seed(a.seed)
np.random.seed(a.seed)
random.seed(a.seed)
loadCheckpointOption(a.mode, a.checkpoint) #loads so that I don't mix up options and it generates data corresponding to this training
config = tf.ConfigProto()
if not os.path.exists(a.output_dir):
os.makedirs(a.output_dir)
with open(os.path.join(a.output_dir, "options.json"), "w") as f:
f.write(json.dumps(vars(a), sort_keys=True, indent=4))
data = dataReader.dataset(a.input_dir, imageFormat = a.imageFormat, trainFolder = a.trainFolder, testFolder = a.testFolder, nbTargetsToRead = a.nbTargets, tileSize=TILE_SIZE, inputImageSize=a.input_size, batchSize=a.batch_size, fixCrop = (a.mode == "test"), mixMaterials = (a.mode == "train" or a.mode == "finetune"), logInput = a.useLog, useAmbientLight = a.useAmbientLight, useAugmentationInRenderings = not a.NoAugmentationInRenderings)
# Populate data
data.loadPathList(a.inputMode, a.mode, a.mode == "train" or a.mode == "finetune", inputpythonList)
if a.feedMethod == "render":
if a.mode == "train":
data.populateInNetworkFeedGraph(a.renderingScene, a.jitterLightPos, a.jitterViewPos, shuffle = (a.mode == "train" or a.mode == "finetune"))
elif a.mode == "finetune":
data.populateInNetworkFeedGraphSpatialMix(a.renderingScene, shuffle = False, imageSize = a.input_size)
elif a.feedMethod == "files":
data.populateFeedGraph(shuffle = (a.mode == "train" or a.mode == "finetune"))
if a.mode == "train" or a.mode == "finetune":
with tf.name_scope("recurrentTest"):
dataTest = dataReader.dataset(a.input_dir, imageFormat = a.imageFormat, testFolder = a.testFolder, nbTargetsToRead = a.nbTargets, tileSize=TILE_SIZE, inputImageSize=a.test_input_size, batchSize=a.batch_size, fixCrop = True, mixMaterials = False, logInput = a.useLog, useAmbientLight = a.useAmbientLight, useAugmentationInRenderings = not a.NoAugmentationInRenderings)
dataTest.loadPathList(a.inputMode, "test", False, inputpythonList)
if a.testApproach == "render":
#dataTest.populateInNetworkFeedGraphSpatialMix(a.renderingScene, shuffle = False, imageSize = TILE_SIZE, useSpatialMix=False)
dataTest.populateInNetworkFeedGraph(a.renderingScene, a.jitterLightPos, a.jitterViewPos, shuffle = False)
elif a.testApproach == "files":
dataTest.populateFeedGraph(False)
targetsReshaped = helpers.target_reshape(data.targetBatch)
#CreateModel
model = mod.Model(data.inputBatch, generatorOutputChannels=9)
model.create_model()
if a.mode == "train" or a.mode == "finetune":
testTargetsReshaped = helpers.target_reshape(dataTest.targetBatch)
testmodel = mod.Model(dataTest.inputBatch, generatorOutputChannels=9, reuse_bool=True)
testmodel.create_model()
display_fetches_test, _ = helpers.display_images_fetches(dataTest.pathBatch, dataTest.inputBatch, dataTest.targetBatch, dataTest.gammaCorrectedInputsBatch, testmodel.output, a.nbTargets, a.logOutputAlbedos)
loss = losses.Loss(a.loss, model.output, targetsReshaped, TILE_SIZE, a.batch_size, tf.placeholder(tf.float64, shape=(), name="lr"), a.includeDiffuse, a.nbSpecularRendering, a.nbDiffuseRendering)
loss.createLossGraph()
loss.createTrainVariablesGraph()
#Register Renderings And Loss In Tensorflow
display_fetches, converted_images = helpers.display_images_fetches(data.pathBatch, data.inputBatch, data.targetBatch, data.gammaCorrectedInputsBatch, model.output, a.nbTargets, a.logOutputAlbedos)
if a.mode == "train":
helpers.registerTensorboard(data.pathBatch, converted_images, a.nbTargets, loss.lossValue, a.batch_size, loss.targetsRenderings, loss.outputsRenderings)
#Run either training or test
with tf.name_scope("parameter_count"):
parameter_count = tf.reduce_sum([tf.reduce_prod(tf.shape(v)) for v in tf.trainable_variables()])
saver = tf.train.Saver(max_to_keep=1)
if a.checkpoint is not None:
print("reading model from checkpoint : " + a.checkpoint)
checkpoint = tf.train.latest_checkpoint(a.checkpoint)
partialSaver = helpers.optimistic_saver(checkpoint) #Be careful this will silently not load variables if they are missing from the graph or checkpoint
logdir = a.output_dir if a.summary_freq > 0 else None
sv = tf.train.Supervisor(logdir=logdir, save_summaries_secs=0, saver=None)
with sv.managed_session("", config= config) as sess:
sess.run(data.iterator.initializer)
print("parameter_count =", sess.run(parameter_count))
if a.checkpoint is not None:
print("restoring model from checkpoint : " + a.checkpoint)
partialSaver.restore(sess, checkpoint)
max_steps = 2**32
if a.max_epochs is not None:
max_steps = data.stepsPerEpoch * a.max_epochs
if a.max_steps is not None:
max_steps = a.max_steps
sess.run(data.iterator.initializer)
if a.mode == "test":
filesets = test(sess, data, max_steps, display_fetches, output_dir = a.output_dir)
if a.mode == "train" or a.mode == "finetune":
train(sv, sess, data, max_steps, display_fetches, display_fetches_test, dataTest, saver, loss, a.output_dir)
def main():
if a.seed is None:
a.seed = random.randint(0, 2**31 - 1)
tf.set_random_seed(a.seed)
np.random.seed(a.seed)
random.seed(a.seed)
#Load some options from the checkpoint if we provided one.
loadCheckpointOption()
#If we feed the network with renderings done in the network for a test run, we save the images before, to be able to compare later with other networks on the same testset.
if a.mode == "test" and a.feedMethod == "render":
testHelpers.renderTests(a.input_dir, a.testFolder, a.maxImages, tmpFolder, a.imageFormat, CROP_SIZE, a.nbTargets, a.input_size, a.batch_size, a.renderingScene, a.jitterLightPos, a.jitterViewPos, a.inputMode, a.mode, a.output_dir)
generateTmpData = True
a.nbInputs = a.maxImages
a.feedMethod = "files"
a.testFolder = tmpFolder
a.input_size = CROP_SIZE
backupOutputDir = a.output_dir
#We run the network once if we a training
nbRun = 1
#And as many time as the maximum number of images we want to treat with if testing (to have results with one image, two images, three images etc... to see the improvement)
if a.mode == "test":
nbRun = a.maxImages #1
a.fixImageNb = True
#Now run the network nbRun times.
for runID in range(nbRun):
maxInputNb = a.maxImages
if a.mode == "test":
maxInputNb = runID + 1 #a.maxImages
a.output_dir = os.path.join(backupOutputDir, str(runID))
tf.reset_default_graph()
#Create the output dir if it doesn't exist
if not os.path.exists(a.output_dir):
os.makedirs(a.output_dir)
#Write to the "options" file the different parameters of this run.
with open(os.path.join(a.output_dir, "options.json"), "w") as f:
f.write(json.dumps(vars(a), sort_keys=True, indent=4))
#Create a dataset object
data = dataReader.dataset(a.input_dir, imageType = a.imageFormat, trainFolder = a.trainFolder, testFolder = a.testFolder, inputNumbers = a.nbInputs, maxInputToRead = maxInputNb, nbTargetsToRead = a.nbTargets, cropSize=CROP_SIZE, inputImageSize=a.input_size, batchSize=a.batch_size, fixCrop = (a.mode == "test"), mixMaterials = (a.mode == "train"), fixImageNb = a.fixImageNb, logInput = a.useLog, useAmbientLight = a.useAmbientLight, jitterRenderings = a.jitterRenderings, firstAsGuide = False, useAugmentationInRenderings = not a.NoAugmentationInRenderings, mode = a.mode)
# Populate the list of files the dataset will contain
data.loadPathList(a.inputMode, a.mode, a.mode == "train")
# Depending on wheter we want to render our input data or directly use files, we create the tensorflow data loading system.
if a.feedMethod == "render":
data.populateInNetworkFeedGraph(a.renderingScene, a.jitterLightPos, a.jitterViewPos, a.mode == "test", shuffle = a.mode == "train")
elif a.feedMethod == "files":
data.populateFeedGraph(shuffle = a.mode == "train")
# Here we reshape the input to have all the images in the first dimension (to treat in parallel)
inputReshaped, dyn_batch_size = helpers.input_reshape(data.inputBatch, a.NoMaxPooling, a.maxImages)
if a.mode == "train":
with tf.name_scope("recurrentTest"):
#Initialize different data for tests.
dataTest = dataReader.dataset(a.input_dir, imageType = a.imageFormat, testFolder = a.testFolder, inputNumbers = a.nbInputs, maxInputToRead = a.maxImages, nbTargetsToRead = a.nbTargets, cropSize=CROP_SIZE, inputImageSize=a.input_size, batchSize=a.batch_size, fixCrop = True, mixMaterials = False, fixImageNb = a.fixImageNb, logInput = a.useLog, useAmbientLight = a.useAmbientLight, jitterRenderings = a.jitterRenderings, firstAsGuide = a.firstAsGuide, useAugmentationInRenderings = not a.NoAugmentationInRenderings, mode = a.mode)
dataTest.loadPathList(a.inputMode, "test", False)
if a.feedMethod == "render":
dataTest.populateInNetworkFeedGraph(a.renderingScene, a.jitterLightPos, a.jitterViewPos, True, shuffle = False)
elif a.feedMethod == "files":
dataTest.populateFeedGraph(False)
TestinputReshaped, test_dyn_batch_size = helpers.input_reshape(dataTest.inputBatch, a.NoMaxPooling, a.maxImages)
#Reshape the targets to [?(Batchsize), 256,256,12]
targetsReshaped = helpers.target_reshape(data.targetBatch)
#Create the object to contain the network model.
model = mod.Model(inputReshaped, dyn_batch_size, last_convolutions_channels = last_convs_chans, generatorOutputChannels=64, useCoordConv = a.useCoordConv, firstAsGuide = a.firstAsGuide, NoMaxPooling = a.NoMaxPooling, pooling_type=a.poolingtype)
#Initialize the model.
model.create_model()
if a.mode == "train":
#Initialize the regular test network with different data so that it can run regular test sets.
testTargetsReshaped = helpers.target_reshape(dataTest.targetBatch)
testmodel = mod.Model(TestinputReshaped, test_dyn_batch_size, last_convolutions_channels = last_convs_chans, generatorOutputChannels=64, reuse_bool=True, useCoordConv = a.useCoordConv, firstAsGuide = a.firstAsGuide, NoMaxPooling = a.NoMaxPooling, pooling_type=a.poolingtype)
testmodel.create_model()
#Organize the images we want to retrieve from the test network run
display_fetches_test, _ = helpers.display_images_fetches(dataTest.pathBatch, dataTest.inputBatch, dataTest.targetBatch, dataTest.gammaCorrectedInputsBatch, testmodel.output, a.nbTargets, a.logOutputAlbedos)
# Compute the training network loss.
loss = losses.Loss(a.loss, model.output, targetsReshaped, CROP_SIZE, a.batch_size, tf.placeholder(tf.float64, shape=(), name="lr"), a.includeDiffuse)
loss.createLossGraph()
#Create the training graph part
loss.createTrainVariablesGraph()
#Organize the images we want to retrieve from the train network run
display_fetches, converted_images = helpers.display_images_fetches(data.pathBatch, data.inputBatch, data.targetBatch, data.gammaCorrectedInputsBatch, model.output, a.nbTargets, a.logOutputAlbedos)
if a.mode == "train":
#Register inputs, targets, renderings and loss in Tensorboard
helpers.registerTensorboard(data.pathBatch, converted_images, a.maxImages, a.nbTargets, loss.lossValue, a.batch_size, loss.targetsRenderings, loss.outputsRenderings)
#Compute how many paramters the network has
with tf.name_scope("parameter_count"):
parameter_count = tf.reduce_sum([tf.reduce_prod(tf.shape(v)) for v in tf.trainable_variables()])
#Initialize a saver
saver = tf.train.Saver(max_to_keep=1)
if a.checkpoint is not None:
print("reading model from checkpoint : " + a.checkpoint)
checkpoint = tf.train.latest_checkpoint(a.checkpoint)
partialSaver = helpers.optimistic_saver(checkpoint)
logdir = a.output_dir if a.summary_freq > 0 else None
sv = tf.train.Supervisor(logdir=logdir, save_summaries_secs=0, saver=None)
#helpers.print_trainable()
with sv.managed_session() as sess:
print("parameter_count =", sess.run(parameter_count))
#Loads the checkpoint
if a.checkpoint is not None:
print("restoring model from checkpoint : " + a.checkpoint)
partialSaver.restore(sess, checkpoint)
#Evaluate how many steps to run
max_steps = 2**32
if a.max_epochs is not None:
max_steps = data.stepsPerEpoch * a.max_epochs
if a.max_steps is not None:
max_steps = a.max_steps
#If we want to run a test
if a.mode == "test" or a.mode == "eval":
filesets = test(sess, data, max_steps, display_fetches, output_dir = a.output_dir)
if runID == nbRun - 1 and runID >= 1: #If we are at the last iteration of the test, generate the full html
helpers.writeGlobalHTML(backupOutputDir, filesets, a.nbTargets, a.mode, a.maxImages)
#If we want to train
if a.mode == "train":
train(sv, sess, data, max_steps, display_fetches, display_fetches_test, dataTest, saver, loss)
def __renderInputs(self, materials, renderingScene, jitterLightPos,
jitterViewPos, mixMaterials, isTest, renderSize):
mixedMaterial = materials
if mixMaterials:
alpha = tf.random_uniform([1],
minval=0.1,
maxval=0.9,
dtype=tf.float32,
name="mixAlpha")
#print("mat2: " + str(materials2))
materials1 = materials[::2]
materials2 = materials[1::2]
mixedMaterial = helpers.mixMaterials(materials1, materials2, alpha)
mixedMaterial.set_shape(
[None, self.nbTargetsToRead, renderSize, renderSize, 3])
mixedMaterial = helpers.adaptRougness(mixedMaterial)
#These 3 lines below tries to scale the albedos to get more variety and to randomly flatten the normals to disambiguate the normals and albedos. We did not see strong effect for these.
#if not isTest and self.useAugmentationInRenderings:
# mixedMaterial = helpers.adaptAlbedos(mixedMaterial, self.batchSize)
# mixedMaterial = helpers.adaptNormals(mixedMaterial, self.batchSize)
reshaped_targets_batch = helpers.target_reshape(
mixedMaterial
) #reshape it to be compatible with the rendering algorithm [?, size, size, 12]
nbRenderings = self.maxInputToRead
if not self.fixImageNb:
#If we don't want a constant number of input images, we randomly select a number of input images between 1 and the maximum number of images defined by the user.
nbRenderings = tf.random_uniform([1],
1,
self.maxInputToRead + 1,
dtype=tf.int32)[0]
rendererInstance = renderer.GGXRenderer(includeDiffuse=True)
## Do renderings of the mixedMaterial
targetstoRender = reshaped_targets_batch
pixelsToAdd = 0
targetstoRender = helpers.preprocess(
targetstoRender) #Put targets to -1; 1
surfaceArray = helpers.generateSurfaceArray(
renderSize, pixelsToAdd
) #Generate a grid Y,X between -1;1 to act as the pixel support of the rendering (computer the direction vector between each pixel and the light/view)
#Do the renderings
inputs = helpers.generateInputRenderings(
rendererInstance,
targetstoRender,
self.batchSize,
nbRenderings,
surfaceArray,
renderingScene,
jitterLightPos,
jitterViewPos,
self.useAmbientLight,
useAugmentationInRenderings=self.useAugmentationInRenderings)
#inputs = [helpers.preprocess(input) for input in inputs]
randomTopLeftCrop = tf.zeros([self.batchSize, nbRenderings, 2],
dtype=tf.int32)
averageCrop = 0.0
#If we want to jitter the renderings around (to try to take into account small non alignment), we should handle the material crop a bit differently
#We didn't really manage to get satisfying results with the jittering of renderings. But the code could be useful if this is of interest to Ansys.
if self.jitterRenderings:
randomTopLeftCrop = tf.random_normal(
[self.batchSize, nbRenderings, 2], 0.0,
1.0) #renderSize - self.cropSize, dtype=tf.int32)
randomTopLeftCrop = randomTopLeftCrop * tf.exp(
tf.random_normal(
[self.batchSize], 0.0,
1.0)) #renderSize - self.cropSize, dtype=tf.int32)
randomTopLeftCrop = randomTopLeftCrop - tf.reduce_mean(
randomTopLeftCrop, axis=1, keep_dims=True)
randomTopLeftCrop = tf.round(randomTopLeftCrop)
randomTopLeftCrop = tf.cast(randomTopLeftCrop, dtype=tf.int32)
averageCrop = tf.cast(self.maxJitteringPixels * 0.5,
dtype=tf.int32)
randomTopLeftCrop = randomTopLeftCrop + averageCrop
randomTopLeftCrop = tf.clip_by_value(randomTopLeftCrop, 0,
self.maxJitteringPixels)
totalCropSize = self.cropSize
inputs, targets = helpers.cutSidesOut(inputs, targetstoRender,
randomTopLeftCrop, totalCropSize,
self.firstAsGuide, averageCrop)
print("inputs shape after" + str(inputs.get_shape()))
self.gammaCorrectedInputsBatch = inputs
tf.summary.image("GammadInputs",
helpers.convert(inputs[0, :]),
max_outputs=5)
inputs = tf.pow(inputs, 2.2) # correct gamma
if self.logInput:
inputs = helpers.logTensor(inputs)
inputs = helpers.preprocess(inputs)
targets = helpers.target_deshape(targets, self.nbTargetsToRead)
return targets, inputs
def populateInNetworkFeedGraphSpatialMix(self,renderingScene, shuffle = True, imageSize = 512, useSpatialMix = True):
with tf.name_scope("load_images"):
#Create a tensor out of the list of paths
filenamesTensor = tf.constant(self.pathList)
#Reads a slice of the tensor, for example, if the tensor is of shape [100,2], the slice shape should be [2] (to check if we have problem here)
dataset = tf.data.Dataset.from_tensor_slices(filenamesTensor)
#for each slice apply the __readImages function
dataset = dataset.map(self.__readImagesGT, num_parallel_calls=int(multiprocessing.cpu_count() / 4))
#Authorize repetition of the dataset when one epoch is over.
#shuffle = True
if shuffle:
dataset = dataset.shuffle(buffer_size=16, reshuffle_each_iteration=True)
#set batch size
dataset = dataset.repeat()
toPull = self.batchSize
if useSpatialMix:
toPull = self.batchSize * 2
batched_dataset = dataset.batch(toPull)
batched_dataset = batched_dataset.prefetch(buffer_size=4)
#Create an iterator to be initialized
iterator = batched_dataset.make_initializable_iterator()
#Create the node to retrieve next batch
paths_batch, targets_batch = iterator.get_next()
inputRealSize = imageSize#Should be input image size but changed tmp
if useSpatialMix:
threshold = 0.5
perlinNoise = tf.expand_dims(tf.expand_dims(helpers.generate_perlin_noise_2d((inputRealSize, inputRealSize), (1,1)), axis = -1), axis = 0)
perlinNoise = (perlinNoise + 1.0) * 0.5
perlinNoise = perlinNoise >= threshold
perlinNoise = tf.cast(perlinNoise, tf.float32)
inverted = 1.0 - perlinNoise
materialsMixed1 = targets_batch[::2] * perlinNoise
materialsMixed2 = targets_batch[1::2] * inverted
fullSizeMixedMaterial = materialsMixed1 + materialsMixed2
targets_batch = fullSizeMixedMaterial
paths_batch = paths_batch[::2]
targetstoRender = helpers.target_reshape(targets_batch) #reshape it to be compatible with the rendering algorithm [?, size, size, 12]
nbRenderings = 1
rendererInstance = renderer.GGXRenderer(includeDiffuse = True)
## Do renderings of the mixedMaterial
mixedMaterial = helpers.adaptRougness(targetstoRender)
targetstoRender = helpers.preprocess(targetstoRender) #Put targets to -1; 1
surfaceArray = helpers.generateSurfaceArray(inputRealSize)
inputs_batch = helpers.generateInputRenderings(rendererInstance, targetstoRender, self.batchSize, nbRenderings, surfaceArray, renderingScene, False, False, self.useAmbientLight, useAugmentationInRenderings = self.useAugmentationInRenderings)
targets_batch = helpers.target_deshape(targetstoRender, self.nbTargetsToRead)
self.gammaCorrectedInputsBatch = tf.squeeze(inputs_batch, [1])
#tf.summary.image("GammadInputs", helpers.convert(inputs[0, :]), max_outputs=5)
inputs_batch = tf.pow(inputs_batch, 2.2) # correct gamma
if self.logInput:
inputs_batch = helpers.logTensor(inputs_batch)
#Do the random crop, if the crop if fix, crop in the middle
if inputRealSize > self.tileSize:
if self.fixCrop:
xyCropping = (inputRealSize - self.tileSize) // 2
xyCropping = [xyCropping, xyCropping]
else:
xyCropping = tf.random_uniform([1], 0, inputRealSize - self.tileSize, dtype=tf.int32)
inputs_batch = inputs_batch[:, :, xyCropping[0] : xyCropping[0] + self.tileSize, xyCropping[0] : xyCropping[0] + self.tileSize, :]
targets_batch = targets_batch[:,:, xyCropping[0] : xyCropping[0] + self.tileSize, xyCropping[0] : xyCropping[0] + self.tileSize, :]
#Set shapes
inputs_batch = tf.squeeze(inputs_batch, [1]) #Before this the input has a useless dimension in 1 as we have only 1 rendering
inputs_batch.set_shape([None, self.tileSize, self.tileSize, 3])
targets_batch.set_shape([None, self.nbTargetsToRead, self.tileSize, self.tileSize, 3])
#Populate the object
self.stepsPerEpoch = int(math.floor(len(self.pathList) / self.batchSize))
self.inputBatch = inputs_batch
self.targetBatch = targets_batch
self.iterator = iterator
self.pathBatch = paths_batch