def setupScreenShading(settings: inference.RenderSettings):
shading = ScreenSpaceShading(device)
shading.fov(settings.CAM_FOV)
shading.ambient_light_color(np.array(settings.AMBIENT_LIGHT_COLOR))
shading.diffuse_light_color(np.array(settings.DIFFUSE_LIGHT_COLOR))
shading.specular_light_color(np.array(settings.SPECULAR_LIGHT_COLOR))
shading.specular_exponent(settings.SPECULAR_EXPONENT)
shading.light_direction(np.array(settings.LIGHT_DIRECTION))
shading.material_color(np.array(settings.MATERIAL_COLOR))
shading.ambient_occlusion(1.0)
shading.inverse_ao = False
return shading
camera.currentDistance = DATASETS[i]['distance']
camera.currentPitch = DATASETS[i]['pitch']
camera.orientation = DATASETS[i]['orientation']
material = inference.Material(DATASETS[i]['iso'])
renderer_path = RENDERER_CPU if DATASETS[i]['file'].endswith(
'vdb') else RENDERER_GPU
data_dir = DATA_DIR_CPU if DATASETS[i]['file'].endswith(
'vdb') else DATA_DIR_GPU
datasetfile = os.path.join(data_dir, DATASETS[i]['file'])
print('Open', datasetfile)
renderer = inference.Renderer(renderer_path, datasetfile, material, camera)
time.sleep(5)
renderer.send_command("aoradius=%5.3f\n" % float(AO_RADIUS))
# create shading
shading = ScreenSpaceShading(torch.device('cpu'))
shading.fov(30)
shading.light_direction(np.array([0.1, 0.1, 1.0]))
shading.ambient_light_color(np.array(DATASETS[i]['ambient']) / 255.0)
shading.diffuse_light_color(np.array(DATASETS[i]['diffuse']) / 255.0)
shading.specular_light_color(np.array(DATASETS[i]['specular']) / 255.0)
shading.specular_exponent(SPECULAR_EXPONENT)
shading.material_color(np.array(DATASETS[i]['material']) / 255.0)
shading.ambient_occlusion(AO_STRENGTH)
shading.background(np.array(BACKGROUND))
# render each model
for k, m in enumerate(MODELS):
print('Render', m['name'])
p = m['path']
outputName = os.path.join(
OUTPUT_FOLDER, "%s.%s.mp4" % (DATASETS[i]['name'], m['name']))
class LossNetUnshaded(nn.Module):
"""
Main Loss Module for unshaded data.
device: cpu or cuda
opt: command line arguments (Namespace object) with:
- losses: list of loss terms with weighting as string
Format: <loss>:<target>:<weighting>
with: loss in {l1, l2, perceptual, gan}
target in {mask, normal, color, ao, all}, 'all' is only allowed for GAN
weighting a positive number
- further parameters depending on the losses
"""
def __init__(self, device, input_channels, output_channels, high_res, padding, opt):
super().__init__()
self.padding = padding
self.upsample = opt.upsample
assert input_channels == 5 # mask, normalX, normalY, normalZ, depth
assert output_channels == 6 # mask, normalX, normalY, normalZ, depth, ambient occlusion
#List of tuples (name, weight or None)
self.loss_list = [s.split(':') for s in opt.losses.split(',')]
# Build losses and weights
builder = LossBuilder(device)
self.loss_dict = {}
self.weight_dict = {}
self.loss_dict['mse'] = builder.mse() #always use mse for psnr
self.weight_dict[('mse','color')] = 0.0
content_layers = []
style_layers = []
self.has_discriminator = False
self.has_style_or_content_loss = False
self.has_temporal_l2_loss = False
for entry in self.loss_list:
if len(entry)<2:
raise ValueError("illegal format for loss list: " + entry)
name = entry[0]
target = entry[1]
weight = entry[2] if len(entry)>2 else None
if target!='mask' and target!='normal' and target!='color' and target!='ao' and target!='depth' and target!='all':
raise ValueError("Unknown target: " + target)
if 'mse'==name or 'l2'==name or 'l2_loss'==name:
self.weight_dict[('mse',target)] = float(weight) if weight is not None else 1.0
elif 'l1'==name or 'l1_loss'==name:
self.loss_dict['l1'] = builder.l1_loss()
self.weight_dict[('l1',target)] = float(weight) if weight is not None else 1.0
elif 'tl2'==name or 'temp-l2'==name:
self.loss_dict['temp-l2'] = builder.mse()
self.weight_dict[('temp-l2',target)] = float(weight) if weight is not None else 1.0
self.has_temporal_l2_loss = True
elif 'l2-ds'==name:
self.loss_dict['l2-ds'] = builder.downsample_loss(
'l2', opt.upscale_factor, 'bilinear')
self.weight_dict[('l2-ds',target)] = float(weight) if weight is not None else 1.0
elif 'l1-ds'==name:
self.loss_dict['l1-ds'] = builder.downsample_loss(
'l1', opt.upscale_factor, 'bilinear')
self.weight_dict[('l1-ds',target)] = float(weight) if weight is not None else 1.0
elif 'perceptual'==name:
content_layers = [(s.split(':')[0],float(s.split(':')[1])) if ':' in s else (s,1) for s in opt.perceptualLossLayers.split(',')]
self.weight_dict[('perceptual',target)] = float(weight) if weight is not None else 1.0
self.has_style_or_content_loss = True
elif 'texture'==name:
style_layers = [(s.split(':')[0],float(s.split(':')[1])) if ':' in s else (s,1) for s in opt.textureLossLayers.split(',')]
#style_layers = [('conv_1',1), ('conv_3',1), ('conv_5',1)]
self.weight_dict[('texture',target)] = float(weight) if weight is not None else 1.0
self.has_style_or_content_loss = True
elif 'adv'==name or 'gan'==name: #spatial-temporal adversary
assert target=='all'
self.discriminator, self.adv_loss = builder.gan_loss(
opt.discriminator, high_res,
26, #5+5+8+8
opt)
self.weight_dict[('adv',target)] = float(weight) if weight is not None else 1.0
self.discriminator_use_previous_image = True
self.discriminator_clip_weights = False
self.has_discriminator = True
elif 'tgan'==name: #temporal adversary, current high-res + previous high-res
assert target=='all'
self.temp_discriminator, self.temp_adv_loss = builder.gan_loss(
opt.discriminator, high_res,
8+8,
opt)
self.weight_dict[('tgan',target)] = float(weight) if weight is not None else 1.0
self.has_discriminator = True
elif 'sgan'==name: #spatial adversary, current high-res + current input
assert target=='all'
self.spatial_discriminator, self.spatial_adv_loss = builder.gan_loss(
opt.discriminator, high_res,
5+8,
opt)
self.weight_dict[('sgan',target)] = float(weight) if weight is not None else 1.0
self.has_discriminator = True
else:
raise ValueError('unknown loss %s'%name)
if self.has_style_or_content_loss:
self.pt_loss, self.style_losses, self.content_losses = \
builder.get_style_and_content_loss(dict(content_layers), dict(style_layers))
self.loss_dict = nn.ModuleDict(self.loss_dict)
print('Loss weights:', self.weight_dict)
self.shading = ScreenSpaceShading(device)
self.shading.fov(30)
self.shading.ambient_light_color(np.array([opt.lossAmbient, opt.lossAmbient, opt.lossAmbient]))
self.shading.diffuse_light_color(np.array([opt.lossDiffuse, opt.lossDiffuse, opt.lossDiffuse]))
self.shading.specular_light_color(np.array([opt.lossSpecular, opt.lossSpecular, opt.lossSpecular]))
self.shading.specular_exponent(16)
self.shading.enable_specular = False
self.shading.light_direction(np.array([0.0, 0.0, 1.0]))
self.shading.material_color(np.array([1.0, 1.0, 1.0]))
self.shading.ambient_occlusion(opt.lossAO)
print('LossNet: ambient occlusion strength:', opt.lossAO)
def get_discr_parameters(self):
params = []
if hasattr(self, 'discriminator'):
params = params + list(self.discriminator.parameters())
if hasattr(self, 'temp_discriminator'):
params = params + list(self.temp_discriminator.parameters())
if hasattr(self, 'spatial_discriminator'):
params = params + list(self.spatial_discriminator.parameters())
return params
def discr_eval(self):
if hasattr(self, 'discriminator'): self.discriminator.eval()
if hasattr(self, 'temp_discriminator'): self.temp_discriminator.eval()
if hasattr(self, 'spatial_discriminator'): self.spatial_discriminator.eval()
def discr_train(self):
if hasattr(self, 'discriminator'): self.discriminator.train()
if hasattr(self, 'temp_discriminator'): self.temp_discriminator.train()
if hasattr(self, 'spatial_discriminator'): self.spatial_discriminator.train()
def print_summary(self, gt_shape, pred_shape, input_shape, prev_pred_warped_shape, num_batches, device):
#Print networks for VGG + Discriminator
import torchsummary
res = gt_shape[1]
if 'perceptual' in self.weight_dict.keys() or 'texture' in self.weight_dict.keys():
print('VGG (Perceptual + Style loss)')
torchsummary.summary(self.pt_loss, (3, res, res), 2*num_batches, device=device.type)
if hasattr(self, 'discriminator'):
print('Discriminator:')
if self.discriminator_use_previous_image:
#2x mask+normal+color+ao
input_images_shape = (16, res, res)
else:
# mask+normal+color+ao
input_images_shape = (8, res, res)
torchsummary.summary(
self.discriminator,
input_images_shape,
2*num_batches,
device=device.type)
@staticmethod
def pad(img, border):
"""
overwrites the border of 'img' with zeros.
The size of the border is specified by 'border'.
The output size is not changed.
"""
if border==0:
return img
b,c,h,w = img.shape
img_crop = img[:,:,border:h-border,border:h-border]
img_pad = F.pad(img_crop, (border, border, border, border), 'constant', 0)
_,_,h2,w2 = img_pad.shape
assert(h==h2)
assert(w==w2)
return img_pad
#@profile
def forward(self, gt, pred, input, prev_input_warped, prev_pred_warped):
"""
gt: ground truth high resolution image (B x C=output_channels x 4W x 4H)
pred: predicted high resolution image (B x C=output_channels x 4W x 4H)
input: upsampled low resolution input image (B x C=input_channels x 4W x 4H)
Only used for the discriminator, can be None if only the other losses are used
prev_input_warped: upsampled, warped previous input image
prev_pred_warped: predicted image from the previous frame warped by the flow
Shape: B x Cout x 4W x 4H
Only used for temporal losses, can be None if only the other losses are used
"""
B, Cout, Hhigh, Whigh = gt.shape
assert Cout == 6
assert gt.shape == pred.shape
# zero border padding
gt = LossNetUnshaded.pad(gt, self.padding)
pred = LossNetUnshaded.pad(pred, self.padding)
if prev_pred_warped is not None:
prev_pred_warped = LossNetUnshaded.pad(prev_pred_warped, self.padding)
# extract mask and normal
gt_mask = gt[:,0:1,:,:]
gt_mask_clamp = torch.clamp(gt_mask*0.5 + 0.5, 0, 1)
gt_normal = ScreenSpaceShading.normalize(gt[:,1:4,:,:], dim=1)
gt_depth = gt[:,4:5,:,:]
gt_ao = gt[:,5:6,:,:]
pred_mask = pred[:,0:1,:,:]
pred_mask_clamp = torch.clamp(pred_mask*0.5 + 0.5, 0, 1)
pred_normal = ScreenSpaceShading.normalize(pred[:,1:4,:,:], dim=1)
pred_depth = pred[:,4:5,:,:]
pred_ao = pred[:,5:6,:,:]
input_mask = input[:,0:1,:,:]
input_mask_clamp = torch.clamp(input_mask*0.5 + 0.5, 0, 1)
input_normal = ScreenSpaceShading.normalize(input[:,1:4,:,:], dim=1)
input_depth = input[:,4:5,:,:]
input_ao = None #not available
# compute color output
gt_color = self.shading(gt)
pred_color = self.shading(pred)
input_color = self.shading(input)
generator_loss = 0.0
loss_values = {}
# normal, simple losses, uses gt+pred
for name in ['mse','l1']:
if (name,'mask') in self.weight_dict.keys():
loss = self.loss_dict[name](gt_mask, pred_mask)
loss_values[(name,'mask')] = loss.item()
generator_loss += self.weight_dict[(name,'mask')] * loss
if (name,'normal') in self.weight_dict.keys():
loss = self.loss_dict[name](gt_normal*gt_mask_clamp, pred_normal*gt_mask_clamp)
loss_values[(name,'normal')] = loss.item()
generator_loss += self.weight_dict[(name,'normal')] * loss
if (name,'ao') in self.weight_dict.keys():
loss = self.loss_dict[name](gt_ao*gt_mask_clamp, pred_ao*gt_mask_clamp)
loss_values[(name,'ao')] = loss.item()
generator_loss += self.weight_dict[(name,'ao')] * loss
if (name,'depth') in self.weight_dict.keys():
loss = self.loss_dict[name](gt_depth*gt_mask_clamp, pred_depth*gt_mask_clamp)
loss_values[(name,'depth')] = loss.item()
generator_loss += self.weight_dict[(name,'depth')] * loss
if (name,'color') in self.weight_dict.keys():
loss = self.loss_dict[name](gt_color, pred_color)
loss_values[(name,'color')] = loss.item()
generator_loss += self.weight_dict[(name,'color')] * loss
# downsample loss, use input+pred
# TODO: input is passed in upsampled version, but this introduces more errors
# Better: input low-res input and use the 'low_res_gt=True' option in downsample_loss
# This requires the input to be upsampled here for the GAN.
for name in ['l2-ds', 'l1-ds']:
if (name,'mask') in self.weight_dict.keys():
loss = self.loss_dict[name](input_mask, pred_mask)
loss_values[(name,'mask')] = loss.item()
generator_loss += self.weight_dict[(name,'mask')] * loss
if (name,'normal') in self.weight_dict.keys():
loss = self.loss_dict[name](input_normal*input_mask_clamp, pred_normal*input_mask_clamp)
loss_values[(name,'normal')] = loss.item()
generator_loss += self.weight_dict[(name,'normal')] * loss
if (name,'ao') in self.weight_dict.keys():
loss = self.loss_dict[name](input_ao*input_mask_clamp, pred_ao*input_mask_clamp)
loss_values[(name,'ao')] = loss.item()
generator_loss += self.weight_dict[(name,'ao')] * loss
if (name,'depth') in self.weight_dict.keys():
loss = self.loss_dict[name](input_depth*input_mask_clamp, pred_depth*input_mask_clamp)
loss_values[(name,'depth')] = loss.item()
generator_loss += self.weight_dict[(name,'depth')] * loss
if (name,'color') in self.weight_dict.keys():
loss = self.loss_dict[name](input_color, pred_color)
loss_values[(name,'color')] = loss.item()
generator_loss += self.weight_dict[(name,'color')] * loss
# special losses: perceptual+texture, uses gt+pred
def compute_perceptual(target, in_pred, in_gt):
if ('perceptual',target) in self.weight_dict.keys() \
or ('texture',target) in self.weight_dict.keys():
style_weight=self.weight_dict.get(('texture',target), 0)
content_weight=self.weight_dict.get(('perceptual',target), 0)
style_score = 0
content_score = 0
input_images = torch.cat([in_gt, in_pred], dim=0)
self.pt_loss(input_images)
for sl in self.style_losses:
style_score += sl.loss
for cl in self.content_losses:
content_score += cl.loss
if ('perceptual',target) in self.weight_dict.keys():
loss_values[('perceptual',target)] = content_score.item()
if ('texture',target) in self.weight_dict.keys():
loss_values[('texture',target)] = style_score.item()
return style_weight * style_score + content_weight * content_score
return 0
generator_loss += compute_perceptual('mask', pred_mask.expand(-1,3,-1,-1)*0.5+0.5, gt_mask.expand(-1,3,-1,-1)*0.5+0.5)
generator_loss += compute_perceptual('normal', (pred_normal*gt_mask_clamp)*0.5+0.5, (gt_normal*gt_mask_clamp)*0.5+0.5)
generator_loss += compute_perceptual('color', pred_color, gt_color)
generator_loss += compute_perceptual('ao', pred_ao.expand(-1,3,-1,-1), gt_ao.expand(-1,3,-1,-1))
generator_loss += compute_perceptual('depth', pred_depth.expand(-1,3,-1,-1), gt_depth.expand(-1,3,-1,-1))
# special: discriminator, uses input+pred+prev_pred_warped
if self.has_discriminator:
pred_with_color = torch.cat([
pred_mask,
pred_normal,
pred_color,
pred_ao], dim=1)
prev_pred_normal = ScreenSpaceShading.normalize(prev_pred_warped[:,1:4,:,:], dim=1)
prev_pred_with_color = torch.cat([
prev_pred_warped[:,0:1,:,:],
prev_pred_normal,
self.shading(torch.cat([
prev_pred_warped[:,0:1,:,:],
prev_pred_normal,
prev_pred_warped[:,4:6,:,:]
], dim=1)),
prev_pred_warped[:,5:6,:,:]
], dim=1)
input_pad = LossNetUnshaded.pad(input, self.padding)
prev_input_warped_pad = LossNetUnshaded.pad(prev_input_warped, self.padding)
pred_with_color_pad = LossNetUnshaded.pad(pred_with_color, self.padding)
prev_pred_warped_pad = LossNetUnshaded.pad(prev_pred_with_color, self.padding)
if ('adv','all') in self.weight_dict.keys(): # spatial-temporal
discr_input = torch.cat([input_pad, prev_input_warped_pad, pred_with_color_pad, prev_pred_warped_pad], dim=1)
gen_loss = self.adv_loss(self.discriminator(discr_input))
loss_values['discr_pred'] = gen_loss.item()
generator_loss += self.weight_dict[('adv','all')] * gen_loss
if ('tgan','all') in self.weight_dict.keys(): #temporal
discr_input = torch.cat([pred_with_color_pad, prev_pred_warped_pad], dim=1)
gen_loss = self.temp_adv_loss(self.temp_discriminator(discr_input))
loss_values['temp_discr_pred'] = gen_loss.item()
generator_loss += self.weight_dict[('tgan','all')] * gen_loss
if ('sgan','all') in self.weight_dict.keys(): #spatial
discr_input = torch.cat([input_pad, pred_with_color_pad], dim=1)
gen_loss = self.spatial_adv_loss(self.spatial_discriminator(discr_input))
loss_values['spatial_discr_pred'] = gen_loss.item()
generator_loss += self.weight_dict[('sgan','all')] * gen_loss
# special: temporal l2 loss, uses input (for the mask) + pred + prev_warped
if self.has_temporal_l2_loss:
prev_pred_mask = prev_pred_warped[:,0:1,:,:]
prev_pred_normal = ScreenSpaceShading.normalize(prev_pred_warped[:,1:4,:,:], dim=1)
if ('temp-l2','mask') in self.weight_dict.keys():
loss = self.loss_dict['temp-l2'](pred_mask, prev_pred_mask)
loss_values[('temp-l2','mask')] = loss.item()
generator_loss += self.weight_dict[('temp-l2','mask')] * loss
if ('temp-l2','normal') in self.weight_dict.keys():
loss = self.loss_dict['temp-l2'](
pred_normal*gt_mask_clamp,
prev_pred_normal*gt_mask_clamp)
loss_values[('temp-l2','normal')] = loss.item()
generator_loss += self.weight_dict[('temp-l2','normal')] * loss
if ('temp-l2','ao') in self.weight_dict.keys():
prev_pred_ao = prev_pred_warped[:,5:6,:,:]
loss = self.loss_dict['temp-l2'](
pred_ao*gt_mask_clamp,
prev_pred_ao*gt_mask_clamp)
loss_values[('temp-l2','ao')] = loss.item()
generator_loss += self.weight_dict[('temp-l2','ao')] * loss
if ('temp-l2','depth') in self.weight_dict.keys():
prev_pred_depth = prev_pred_warped[:,4:5,:,:]
loss = self.loss_dict['temp-l2'](
pred_depth*gt_mask_clamp,
prev_pred_depth*gt_mask_clamp)
loss_values[('temp-l2','depth')] = loss.item()
generator_loss += self.weight_dict[('temp-l2','depth')] * loss
if ('temp-l2','color') in self.weight_dict.keys():
prev_pred_color = self.shading(prev_pred_warped)
loss = self.loss_dict['temp-l2'](pred_color, prev_pred_color)
loss_values[('temp-l2','color')] = loss.item()
generator_loss += self.weight_dict[('temp-l2','color')] * loss
return generator_loss, loss_values
"""deprecated"""
def evaluate_discriminator(self,
current_input, previous_input,
current_output, previous_output):
"""
Discriminator takes the following inputs:
- current input upsampled (B x 5 (mask+normal+depth) x H x W)
- previous input warped upsampled (B x 5 x H x W)
- current prediction with color (B x 8 (mask+normal+ao+color) x H x W)
- previous prediction warped with color (B x 8 x H x W)
All tensors are already padded
Returns the score of the discriminator, averaged over the batch
"""
assert current_input.shape[1] == 5
assert previous_input.shape[1] == 5
assert current_output.shape[1] == 8
assert previous_output.shape[1] == 8
B, _, H, W = current_input.shape
input = torch.cat([current_input, previous_input, current_output, previous_output], dim=1)
return self.adv_loss(self.discriminator(input))
def train_discriminator(self, input, gt_high,
previous_input, gt_prev_warped,
pred_high, pred_prev_warped):
"""
All inputs are in high resolution.
input: B x 5 x H x W (mask+normal+depth)
gt_high: B x 6 x H x W
previous_input: B x 5 x H x W
gt_prev_warped: B x 6 x H x W
pred_high: B x 6 x H x W
pred_prev_warped: B x 6 x H x W
"""
assert self.has_discriminator
def colorize_and_pad(tensor):
assert tensor.shape[1]==6
mask = tensor[:,0:1,:,:]
normal = ScreenSpaceShading.normalize(tensor[:,1:4,:,:], dim=1)
depth_ao = tensor[:,4:6,:,:]
ao = tensor[:,5:6,:,:]
color = self.shading(torch.cat([mask, normal, depth_ao], dim=1))
tensor_with_color = torch.cat([mask, normal, ao, color], dim=1)
return LossNetUnshaded.pad(tensor_with_color, self.padding)
# assemble input
input = LossNetUnshaded.pad(input, self.padding)
gt_high = colorize_and_pad(gt_high)
pred_high = colorize_and_pad(pred_high)
previous_input = LossNetUnshaded.pad(previous_input, self.padding)
gt_prev_warped = colorize_and_pad(gt_prev_warped)
pred_prev_warped = colorize_and_pad(pred_prev_warped)
# compute losses
discr_loss = 0
gt_score = 0
pred_score = 0
if ('adv','all') in self.weight_dict.keys(): # spatial-temporal
gt_input = torch.cat([
input,
previous_input,
gt_high,
gt_prev_warped], dim=1)
pred_input = torch.cat([
input,
previous_input,
pred_high,
pred_prev_warped], dim=1)
discr_loss0, gt_score0, pred_score0 = self.adv_loss.train_discr(
gt_input, pred_input, self.discriminator)
discr_loss += self.weight_dict[('adv','all')] * discr_loss0
gt_score += self.weight_dict[('adv','all')] * gt_score0
pred_score += self.weight_dict[('adv','all')] * pred_score0
if ('tgan','all') in self.weight_dict.keys(): # temporal
gt_input = torch.cat([
gt_high,
gt_prev_warped], dim=1)
pred_input = torch.cat([
pred_high,
pred_prev_warped], dim=1)
discr_loss0, gt_score0, pred_score0 = self.temp_adv_loss.train_discr(
gt_input, pred_input, self.temp_discriminator)
discr_loss += self.weight_dict[('tgan','all')] * discr_loss0
gt_score += self.weight_dict[('tgan','all')] * gt_score0
pred_score += self.weight_dict[('tgan','all')] * pred_score0
if ('sgan','all') in self.weight_dict.keys(): # spatial-temporal
gt_input = torch.cat([
input,
gt_high], dim=1)
pred_input = torch.cat([
input,
pred_high], dim=1)
discr_loss0, gt_score0, pred_score0 = self.spatial_adv_loss.train_discr(
gt_input, pred_input, self.spatial_discriminator)
discr_loss += self.weight_dict[('sgan','all')] * discr_loss0
gt_score += self.weight_dict[('sgan','all')] * gt_score0
pred_score += self.weight_dict[('sgan','all')] * pred_score0
return discr_loss, gt_score, pred_score
def run():
torch.ops.load_library("./Renderer.dll")
#########################
# CONFIGURATION
#########################
if 0:
OUTPUT_FOLDER = "../result-stats/adaptiveIso2/"
DATASET_PREFIX = "D:/VolumeSuperResolution-InputData/"
DATASETS = [
#("Ejecta", "gt-rendering-ejecta-v2-test.hdf5"),
("RM", "gt-rendering-rm-v1.hdf5"),
#("Human", "gt-rendering-human-v1.hdf5"),
#("Thorax", "gt-rendering-thorax-v1.hdf5"),
]
NETWORK_DIR = "D:/VolumeSuperResolution/adaptive-modeldir/"
NETWORKS = [ #suffixed with _importance.pt and _recon.pt
#("adaptive011", "adaptive011_epoch500"), #title, file prefix
("adaptive019", "adaptive019_epoch470"),
("adaptive023", "adaptive023_epoch300")
]
SAMPLING_FILE = "D:/VolumeSuperResolution-InputData/samplingPattern.hdf5"
SAMPLING_PATTERNS = ['halton', 'plastic', 'random']
HEATMAP_MIN = [0.01, 0.05, 0.2]
HEATMAP_MEAN = [0.05, 0.1, 0.2, 0.5]
UPSCALING = 8 # = networkUp * postUp
IMPORTANCE_BORDER = 8
LOSS_BORDER = 32
BATCH_SIZE = 8
elif 0:
OUTPUT_FOLDER = "../result-stats/adaptiveIsoEnhance6/"
DATASET_PREFIX = "D:/VolumeSuperResolution-InputData/"
DATASETS = [
("Ejecta", "gt-rendering-ejecta-v2-test.hdf5"),
#("RM", "gt-rendering-rm-v1.hdf5"),
#("Human", "gt-rendering-human-v1.hdf5"),
#("Thorax", "gt-rendering-thorax-v1.hdf5"),
#("Head", "gt-rendering-head.hdf5"),
]
NETWORK_DIR = "D:/VolumeSuperResolution/adaptive-modeldir/"
NETWORKS = [ #suffixed with _importance.pt and _recon.pt
#title, file prefix
#("U-Net (5-4)", "sizes/size5-4_epoch500"),
#("Enhance-Net (epoch 50)", "enhance2_imp050_epoch050"),
#("Enhance-Net (epoch 400)", "enhance2_imp050_epoch400"),
#("Enhance-Net (Thorax)", "enhance_imp050_Thorax_epoch200"),
#("Enhance-Net (RM)", "enhance_imp050_RM_epoch200"),
#("Imp100", "enhance4_imp100_epoch300"),
#("Imp100res", "enhance4_imp100res_epoch230"),
("Imp100res+N", "enhance4_imp100res+N_epoch300"),
("Imp100+N", "enhance4_imp100+N_epoch300"),
#("Imp100+N-res", "enhance4_imp100+N-res_epoch300"),
#("Imp100+N-resInterp", "enhance4_imp100+N-resInterp_epoch300"),
#("U-Net (5-4)", "size5-4_epoch500"),
#("U-Net (5-3)", "size5-3_epoch500"),
#("U-Net (4-4)", "size4-4_epoch500"),
]
# Test if it is better to post-train with dense networks and PDE inpainting
POSTTRAIN_NETWORK_DIR = "D:/VolumeSuperResolution/dense-modeldir/"
POSTTRAIN_NETWORKS = [
# title, file suffix to POSTTRAIN_NETWORK_DIR, inpainting {'fast', 'pde'}
#("Enhance PDE (post)", "inpHv2-pde05-epoch200.pt", "pde")
]
SAMPLING_FILE = "D:/VolumeSuperResolution-InputData/samplingPattern.hdf5"
SAMPLING_PATTERNS = ['plastic']
HEATMAP_MIN = [0.002]
HEATMAP_MEAN = [
0.05
] #[0.01, 0.02, 0.03, 0.04, 0.06, 0.08, 0.1, 0.2, 0.3, 0.5, 0.8, 1.0]
USE_BINARY_SEARCH_ON_MEAN = True
UPSCALING = 8 # = networkUp * postUp
IMPORTANCE_BORDER = 8
LOSS_BORDER = 32
BATCH_SIZE = 4
elif 0:
OUTPUT_FOLDER = "../result-stats/adaptiveIsoEnhance5Sampling/"
DATASET_PREFIX = "D:/VolumeSuperResolution-InputData/"
DATASETS = [
("Ejecta", "gt-rendering-ejecta-v2-test.hdf5"),
]
NETWORK_DIR = "D:/VolumeSuperResolution/adaptive-modeldir/"
NETWORKS = [ #suffixed with _importance.pt and _recon.pt
#title, file prefix
("Enhance-Net (epoch 400)", "enhance2_imp050_epoch400"),
]
# Test if it is better to post-train with dense networks and PDE inpainting
POSTTRAIN_NETWORK_DIR = "D:/VolumeSuperResolution/dense-modeldir/"
POSTTRAIN_NETWORKS = [
# title, file suffix to POSTTRAIN_NETWORK_DIR, inpainting {'fast', 'pde'}
#("Enhance PDE (post)", "inpHv2-pde05-epoch200.pt", "pde")
]
SAMPLING_FILE = "D:/VolumeSuperResolution-InputData/samplingPattern.hdf5"
SAMPLING_PATTERNS = ['halton', 'plastic', 'random', 'regular']
#SAMPLING_PATTERNS = ['regular']
HEATMAP_MIN = [0.002]
HEATMAP_MEAN = [0.05]
USE_BINARY_SEARCH_ON_MEAN = True
UPSCALING = 8 # = networkUp * postUp
IMPORTANCE_BORDER = 8
LOSS_BORDER = 32
BATCH_SIZE = 4
elif 1:
OUTPUT_FOLDER = "../result-stats/adaptiveIsoEnhance8Sampling/"
DATASET_PREFIX = "D:/VolumeSuperResolution-InputData/"
DATASETS = [
("Ejecta", "gt-rendering-ejecta-v2-test.hdf5"),
#("RM", "gt-rendering-rm-v1.hdf5"),
#("Human", "gt-rendering-human-v1.hdf5"),
#("Thorax", "gt-rendering-thorax-v1.hdf5"),
#("Head", "gt-rendering-head.hdf5"),
]
NETWORK_DIR = "D:/VolumeSuperResolution/adaptive-modeldir/"
NETWORKS = [ #suffixed with _importance.pt and _recon.pt
#title, file prefix
("regular", "enhance7_regular_epoch190"),
("random", "enhance7_random_epoch190"),
("halton", "enhance7_halton_epoch190"),
("plastic", "enhance7_plastic_epoch190"),
]
# Test if it is better to post-train with dense networks and PDE inpainting
POSTTRAIN_NETWORK_DIR = "D:/VolumeSuperResolution/dense-modeldir/"
POSTTRAIN_NETWORKS = [
# title, file suffix to POSTTRAIN_NETWORK_DIR, inpainting {'fast', 'pde'}
#("Enhance PDE (post)", "inpHv2-pde05-epoch200.pt", "pde")
]
SAMPLING_FILE = "D:/VolumeSuperResolution-InputData/samplingPattern.hdf5"
SAMPLING_PATTERNS = ['regular', 'random', 'halton', 'plastic']
HEATMAP_MIN = [0.002]
HEATMAP_MEAN = [
0.05
] #[0.01, 0.02, 0.03, 0.04, 0.06, 0.08, 0.1, 0.2, 0.3, 0.5, 0.8, 1.0]
USE_BINARY_SEARCH_ON_MEAN = True
UPSCALING = 8 # = networkUp * postUp
IMPORTANCE_BORDER = 8
LOSS_BORDER = 32
BATCH_SIZE = 4
elif 0:
OUTPUT_FOLDER = "../result-stats/adaptiveIsoImp/"
DATASET_PREFIX = "D:/VolumeSuperResolution-InputData/"
DATASETS = [
("Ejecta", "gt-rendering-ejecta-v2-test.hdf5"),
#("RM", "gt-rendering-rm-v1.hdf5"),
#("Human", "gt-rendering-human-v1.hdf5"),
#("Thorax", "gt-rendering-thorax-v1.hdf5"),
]
NETWORK_DIR = "D:/VolumeSuperResolution/adaptive-modeldir/imp/"
NETWORKS = [ #suffixed with _importance.pt and _recon.pt
#("adaptive011", "adaptive011_epoch500"), #title, file prefix
("imp005", "imp005_epoch500"),
("imp010", "imp010_epoch500"),
("imp020", "imp020_epoch500"),
("imp050", "imp050_epoch500"),
]
SAMPLING_FILE = "D:/VolumeSuperResolution-InputData/samplingPattern.hdf5"
SAMPLING_PATTERNS = ['halton']
HEATMAP_MIN = [0.002]
HEATMAP_MEAN = [0.005, 0.01, 0.02, 0.05, 0.1]
USE_BINARY_SEARCH_ON_MEAN = True
UPSCALING = 8 # = networkUp * postUp
IMPORTANCE_BORDER = 8
LOSS_BORDER = 32
BATCH_SIZE = 16
#########################
# LOADING
#########################
device = torch.device("cuda")
# Load Networks
IMPORTANCE_BASELINE1 = "ibase1"
IMPORTANCE_BASELINE2 = "ibase2"
IMPORTANCE_BASELINE3 = "ibase3"
RECON_BASELINE = "rbase"
# load importance model
print("load importance networks")
class ImportanceModel:
def __init__(self, file):
if file == IMPORTANCE_BASELINE1:
self._net = importance.UniformImportanceMap(1, 0.5)
self._upscaling = 1
self._name = "constant"
self.disableTemporal = True
self._requiresPrevious = False
elif file == IMPORTANCE_BASELINE2:
self._net = importance.GradientImportanceMap(
1, (1, 1), (2, 1), (3, 1))
self._upscaling = 1
self._name = "curvature"
self.disableTemporal = True
self._requiresPrevious = False
else:
self._name = file[0]
file = os.path.join(NETWORK_DIR, file[1] + "_importance.pt")
extra_files = torch._C.ExtraFilesMap()
extra_files['settings.json'] = ""
self._net = torch.jit.load(file,
map_location=device,
_extra_files=extra_files)
settings = json.loads(extra_files['settings.json'])
self._upscaling = settings['networkUpscale']
self._requiresPrevious = settings.get("requiresPrevious",
False)
self.disableTemporal = settings.get("disableTemporal", True)
def networkUpscaling(self):
return self._upscaling
def name(self):
return self._name
def __repr__(self):
return self.name()
def call(self, input, prev_warped_out):
if self._requiresPrevious:
input = torch.cat([
input,
models.VideoTools.flatten_high(prev_warped_out,
self._upscaling)
],
dim=1)
input = F.pad(input, [IMPORTANCE_BORDER] * 4, 'constant', 0)
output = self._net(input) # the network call
output = F.pad(output, [-IMPORTANCE_BORDER * self._upscaling] * 4,
'constant', 0)
return output
class LuminanceImportanceModel:
def __init__(self):
self.disableTemporal = True
def setTestFile(self, filename):
importance_file = filename[:-5] + "-luminanceImportance.hdf5"
if os.path.exists(importance_file):
self._exist = True
self._file = h5py.File(importance_file, 'r')
self._dset = self._file['importance']
else:
self._exist = False
self._file = None
self._dset = None
def isAvailable(self):
return self._exist
def setIndices(self, indices: torch.Tensor):
assert len(indices.shape) == 1
self._indices = list(indices.cpu().numpy())
def setTime(self, time):
self._time = time
def networkUpscaling(self):
return UPSCALING
def name(self):
return "luminance-contrast"
def __repr__(self):
return self.name()
def call(self, input, prev_warped_out):
B, C, H, W = input.shape
if not self._exist:
return torch.ones(B,
1,
H,
W,
dtype=input.dtype,
device=input.device)
outputs = []
for idx in self._indices:
outputs.append(
torch.from_numpy(self._dset[idx, self._time,
...]).to(device=input.device))
return torch.stack(outputs, dim=0)
importanceBaseline1 = ImportanceModel(IMPORTANCE_BASELINE1)
importanceBaseline2 = ImportanceModel(IMPORTANCE_BASELINE2)
importanceBaselineLuminance = LuminanceImportanceModel()
importanceModels = [ImportanceModel(f) for f in NETWORKS]
# load reconstruction networks
print("load reconstruction networks")
class ReconstructionModel:
def __init__(self, file):
if file == RECON_BASELINE:
class Inpainting(nn.Module):
def forward(self, x, mask):
input = x[:, 0:6, :, :].contiguous(
) # mask, normal xyz, depth, ao
mask = x[:, 6, :, :].contiguous()
return torch.ops.renderer.fast_inpaint(mask, input)
self._net = Inpainting()
self._upscaling = 1
self._name = "inpainting"
self.disableTemporal = True
else:
self._name = file[0]
file = os.path.join(NETWORK_DIR, file[1] + "_recon.pt")
extra_files = torch._C.ExtraFilesMap()
extra_files['settings.json'] = ""
self._net = torch.jit.load(file,
map_location=device,
_extra_files=extra_files)
self._settings = json.loads(extra_files['settings.json'])
self.disableTemporal = False
requiresMask = self._settings.get('expectMask', False)
if self._settings.get("interpolateInput", False):
self._originalNet = self._net
class Inpainting2(nn.Module):
def __init__(self, orignalNet, requiresMask):
super().__init__()
self._n = orignalNet
self._requiresMask = requiresMask
def forward(self, x, mask):
input = x[:, 0:6, :, :].contiguous(
) # mask, normal xyz, depth, ao
mask = x[:, 6, :, :].contiguous()
inpainted = torch.ops.renderer.fast_inpaint(
mask, input)
x[:, 0:6, :, :] = inpainted
if self._requiresMask:
return self._n(x, mask)
else:
return self._n(x)
self._net = Inpainting2(self._originalNet, requiresMask)
def name(self):
return self._name
def __repr__(self):
return self.name()
def call(self, input, mask, prev_warped_out):
input = torch.cat([input, prev_warped_out], dim=1)
output = self._net(input, mask)
return output
class ReconstructionModelPostTrain:
"""
Reconstruction model that are trained as dense reconstruction networks
after the adaptive training.
They don't recive the sampling mask as input, but can start with PDE-based inpainting
"""
def __init__(self, name: str, model_path: str, inpainting: str):
assert inpainting == 'fast' or inpainting == 'pde', "inpainting must be either 'fast' or 'pde', but got %s" % inpainting
self._inpainting = inpainting
self._name = name
file = os.path.join(POSTTRAIN_NETWORK_DIR, model_path)
extra_files = torch._C.ExtraFilesMap()
extra_files['settings.json'] = ""
self._net = torch.jit.load(file,
map_location=device,
_extra_files=extra_files)
self._settings = json.loads(extra_files['settings.json'])
assert self._settings.get(
'upscale_factor', None) == 1, "selected file is not a 1x SRNet"
self.disableTemporal = False
def name(self):
return self._name
def __repr__(self):
return self.name()
def call(self, input, prev_warped_out):
# no sampling and no AO
input_no_sampling = input[:, 0:5, :, :].contiguous(
) # mask, normal xyz, depth
sampling_mask = input[:, 6, :, :].contiguous()
# perform inpainting
if self._inpainting == 'pde':
inpainted = torch.ops.renderer.pde_inpaint(
sampling_mask,
input_no_sampling,
200,
1e-4,
5,
2, # m0, epsilon, m1, m2
0, # mc -> multigrid recursion count. =0 disables the multigrid hierarchy
9,
0) # ms, m3
else:
inpainted = torch.ops.renderer.fast_inpaint(
sampling_mask, input_no_sampling)
# run network
input = torch.cat([inpainted, prev_warped_out], dim=1)
output = self._net(input)
if isinstance(output, tuple):
output = output[0]
return output
reconBaseline = ReconstructionModel(RECON_BASELINE)
reconModels = [ReconstructionModel(f) for f in NETWORKS]
reconPostModels = [
ReconstructionModelPostTrain(name, file, inpainting)
for (name, file, inpainting) in POSTTRAIN_NETWORKS
]
allReconModels = reconModels + reconPostModels
NETWORK_COMBINATIONS = \
[(importanceBaseline1, reconBaseline), (importanceBaseline2, reconBaseline)] + \
[(importanceBaselineLuminance, reconBaseline)] + \
[(importanceBaseline1, reconNet) for reconNet in allReconModels] + \
[(importanceBaseline2, reconNet) for reconNet in allReconModels] + \
[(importanceBaselineLuminance, reconNet) for reconNet in allReconModels] + \
[(importanceNet, reconBaseline) for importanceNet in importanceModels] + \
list(zip(importanceModels, reconModels)) + \
[(importanceNet, reconPostModel) for importanceNet in importanceModels for reconPostModel in reconPostModels]
#NETWORK_COMBINATIONS = list(zip(importanceModels, reconModels))
print("Network combinations:")
for (i, r) in NETWORK_COMBINATIONS:
print(" %s - %s" % (i.name(), r.name()))
# load sampling patterns
print("load sampling patterns")
with h5py.File(SAMPLING_FILE, 'r') as f:
sampling_pattern = dict([(name, torch.from_numpy(f[name][...]).to(device)) \
for name in SAMPLING_PATTERNS])
# create shading
shading = ScreenSpaceShading(device)
shading.fov(30)
shading.ambient_light_color(np.array([0.1, 0.1, 0.1]))
shading.diffuse_light_color(np.array([1.0, 1.0, 1.0]))
shading.specular_light_color(np.array([0.0, 0.0, 0.0]))
shading.specular_exponent(16)
shading.light_direction(np.array([0.1, 0.1, 1.0]))
shading.material_color(np.array([1.0, 0.3, 0.0]))
AMBIENT_OCCLUSION_STRENGTH = 1.0
shading.ambient_occlusion(1.0)
shading.inverse_ao = False
#heatmap
HEATMAP_CFG = [(min, mean) for min in HEATMAP_MIN for mean in HEATMAP_MEAN
if min < mean]
print("heatmap configs:", HEATMAP_CFG)
#########################
# DEFINE STATISTICS
#########################
ssimLoss = SSIM(size_average=False)
ssimLoss.to(device)
psnrLoss = PSNR()
psnrLoss.to(device)
lpipsColor = lpips.PerceptualLoss(model='net-lin',
net='alex',
use_gpu=True)
MIN_FILLING = 0.05
NUM_BINS = 200
class Statistics:
def __init__(self):
self.histogram_color_withAO = np.zeros(NUM_BINS, dtype=np.float64)
self.histogram_color_noAO = np.zeros(NUM_BINS, dtype=np.float64)
self.histogram_depth = np.zeros(NUM_BINS, dtype=np.float64)
self.histogram_normal = np.zeros(NUM_BINS, dtype=np.float64)
self.histogram_mask = np.zeros(NUM_BINS, dtype=np.float64)
self.histogram_ao = np.zeros(NUM_BINS, dtype=np.float64)
self.histogram_counter = 0
def create_datasets(self, hdf5_file: h5py.File, stats_name: str,
histo_name: str, num_samples: int,
extra_info: dict):
self.expected_num_samples = num_samples
stats_shape = (num_samples, len(list(StatField)))
self.stats_file = hdf5_file.require_dataset(stats_name,
stats_shape,
dtype='f',
exact=True)
self.stats_file.attrs['NumFields'] = len(list(StatField))
for field in list(StatField):
self.stats_file.attrs['Field%d' % field.value] = field.name
for key, value in extra_info.items():
self.stats_file.attrs[key] = value
self.stats_index = 0
histo_shape = (NUM_BINS, len(list(HistoField)))
self.histo_file = hdf5_file.require_dataset(histo_name,
histo_shape,
dtype='f',
exact=True)
self.histo_file.attrs['NumFields'] = len(list(HistoField))
for field in list(HistoField):
self.histo_file.attrs['Field%d' % field.value] = field.name
for key, value in extra_info.items():
self.histo_file.attrs[key] = value
def add_timestep_sample(self, pred_mnda, gt_mnda, sampling_mask):
"""
adds a timestep sample:
pred_mnda: prediction: mask, normal, depth, AO
gt_mnda: ground truth: mask, normal, depth, AO
"""
B = pred_mnda.shape[0]
#shading
shading.ambient_occlusion(AMBIENT_OCCLUSION_STRENGTH)
pred_color_withAO = shading(pred_mnda)
gt_color_withAO = shading(gt_mnda)
shading.ambient_occlusion(0.0)
pred_color_noAO = shading(pred_mnda)
gt_color_noAO = shading(gt_mnda)
#apply border
pred_mnda = pred_mnda[:, :, LOSS_BORDER:-LOSS_BORDER,
LOSS_BORDER:-LOSS_BORDER]
pred_color_withAO = pred_color_withAO[:, :,
LOSS_BORDER:-LOSS_BORDER,
LOSS_BORDER:-LOSS_BORDER]
pred_color_noAO = pred_color_noAO[:, :, LOSS_BORDER:-LOSS_BORDER,
LOSS_BORDER:-LOSS_BORDER]
gt_mnda = gt_mnda[:, :, LOSS_BORDER:-LOSS_BORDER,
LOSS_BORDER:-LOSS_BORDER]
gt_color_withAO = gt_color_withAO[:, :, LOSS_BORDER:-LOSS_BORDER,
LOSS_BORDER:-LOSS_BORDER]
gt_color_noAO = gt_color_noAO[:, :, LOSS_BORDER:-LOSS_BORDER,
LOSS_BORDER:-LOSS_BORDER]
mask = gt_mnda[:, 0:1, :, :] * 0.5 + 0.5
# PSNR
psnr_mask = psnrLoss(pred_mnda[:, 0:1, :, :],
gt_mnda[:, 0:1, :, :]).cpu().numpy()
psnr_normal = psnrLoss(pred_mnda[:, 1:4, :, :],
gt_mnda[:, 1:4, :, :],
mask=mask).cpu().numpy()
psnr_depth = psnrLoss(pred_mnda[:, 4:5, :, :],
gt_mnda[:, 4:5, :, :],
mask=mask).cpu().numpy()
psnr_ao = psnrLoss(pred_mnda[:, 5:6, :, :],
gt_mnda[:, 5:6, :, :],
mask=mask).cpu().numpy()
psnr_color_withAO = psnrLoss(pred_color_withAO,
gt_color_withAO,
mask=mask).cpu().numpy()
psnr_color_noAO = psnrLoss(pred_color_noAO,
gt_color_noAO,
mask=mask).cpu().numpy()
# SSIM
ssim_mask = ssimLoss(pred_mnda[:, 0:1, :, :],
gt_mnda[:, 0:1, :, :]).cpu().numpy()
pred_mnda = gt_mnda + mask * (pred_mnda - gt_mnda)
ssim_normal = ssimLoss(pred_mnda[:, 1:4, :, :],
gt_mnda[:, 1:4, :, :]).cpu().numpy()
ssim_depth = ssimLoss(pred_mnda[:, 4:5, :, :],
gt_mnda[:, 4:5, :, :]).cpu().numpy()
ssim_ao = ssimLoss(pred_mnda[:, 5:6, :, :],
gt_mnda[:, 5:6, :, :]).cpu().numpy()
ssim_color_withAO = ssimLoss(pred_color_withAO,
gt_color_withAO).cpu().numpy()
ssim_color_noAO = ssimLoss(pred_color_noAO,
gt_color_noAO).cpu().numpy()
# Perceptual
lpips_color_withAO = torch.cat([
lpipsColor(
pred_color_withAO[b], gt_color_withAO[b], normalize=True)
for b in range(B)
],
dim=0).cpu().numpy()
lpips_color_noAO = torch.cat([
lpipsColor(
pred_color_noAO[b], gt_color_noAO[b], normalize=True)
for b in range(B)
],
dim=0).cpu().numpy()
# Samples
samples = torch.mean(sampling_mask, dim=(1, 2, 3)).cpu().numpy()
# Write samples to file
for b in range(B):
assert self.stats_index < self.expected_num_samples, "Adding more samples than specified"
self.stats_file[self.stats_index, :] = np.array([
psnr_mask[b], psnr_normal[b], psnr_depth[b], psnr_ao[b],
psnr_color_noAO[b], psnr_color_withAO[b], ssim_mask[b],
ssim_normal[b], ssim_depth[b], ssim_ao[b],
ssim_color_noAO[b], ssim_color_withAO[b],
lpips_color_noAO[b], lpips_color_withAO[b], samples[b]
],
dtype='f')
self.stats_index += 1
# Histogram
self.histogram_counter += 1
mask_diff = F.l1_loss(gt_mnda[:, 0, :, :],
pred_mnda[:, 0, :, :],
reduction='none')
histogram, _ = np.histogram(mask_diff.cpu().numpy(),
bins=NUM_BINS,
range=(0, 1),
density=True)
self.histogram_mask += (
histogram /
(NUM_BINS * B) - self.histogram_mask) / self.histogram_counter
#normal_diff = (-F.cosine_similarity(gt_mnda[0,1:4,:,:], pred_mnda[0,1:4,:,:], dim=0)+1)/2
normal_diff = F.l1_loss(gt_mnda[:, 1:4, :, :],
pred_mnda[:, 1:4, :, :],
reduction='none').sum(dim=0) / 6
histogram, _ = np.histogram(normal_diff.cpu().numpy(),
bins=NUM_BINS,
range=(0, 1),
density=True)
self.histogram_normal += (histogram /
(NUM_BINS * B) - self.histogram_normal
) / self.histogram_counter
depth_diff = F.l1_loss(gt_mnda[:, 4, :, :],
pred_mnda[:, 4, :, :],
reduction='none')
histogram, _ = np.histogram(depth_diff.cpu().numpy(),
bins=NUM_BINS,
range=(0, 1),
density=True)
self.histogram_depth += (
histogram /
(NUM_BINS * B) - self.histogram_depth) / self.histogram_counter
ao_diff = F.l1_loss(gt_mnda[:, 5, :, :],
pred_mnda[:, 5, :, :],
reduction='none')
histogram, _ = np.histogram(ao_diff.cpu().numpy(),
bins=NUM_BINS,
range=(0, 1),
density=True)
self.histogram_ao += (histogram / (NUM_BINS * B) -
self.histogram_ao) / self.histogram_counter
color_diff = F.l1_loss(gt_color_withAO[:, 0, :, :],
pred_color_withAO[:, 0, :, :],
reduction='none')
histogram, _ = np.histogram(color_diff.cpu().numpy(),
bins=NUM_BINS,
range=(0, 1),
density=True)
self.histogram_color_withAO += (
histogram / (NUM_BINS * B) -
self.histogram_color_withAO) / self.histogram_counter
color_diff = F.l1_loss(gt_color_noAO[:, 0, :, :],
pred_color_noAO[:, 0, :, :],
reduction='none')
histogram, _ = np.histogram(color_diff.cpu().numpy(),
bins=NUM_BINS,
range=(0, 1),
density=True)
self.histogram_color_noAO += (
histogram / (NUM_BINS * B) -
self.histogram_color_noAO) / self.histogram_counter
def close_stats_file(self):
self.stats_file.attrs['NumEntries'] = self.stats_index
def write_histogram(self):
"""
After every sample for the current dataset was processed, write
a histogram of the errors in a new file
"""
for i in range(NUM_BINS):
self.histo_file[i, :] = np.array([
i / NUM_BINS, (i + 1) / NUM_BINS, self.histogram_mask[i],
self.histogram_normal[i], self.histogram_depth[i],
self.histogram_ao[i], self.histogram_color_withAO[i],
self.histogram_color_noAO[i]
])
#########################
# DATASET
#########################
class FullResDataset(torch.utils.data.Dataset):
def __init__(self, file):
self.hdf5_file = h5py.File(file, 'r')
self.dset = self.hdf5_file['gt']
print("Dataset shape:", self.dset.shape)
def __len__(self):
return self.dset.shape[0]
def num_timesteps(self):
return self.dset.shape[1]
def __getitem__(self, idx):
return (self.dset[idx, ...], np.array(idx))
#########################
# COMPUTE STATS for each dataset
#########################
for dataset_name, dataset_file in DATASETS:
dataset_file = os.path.join(DATASET_PREFIX, dataset_file)
print("Compute statistics for", dataset_name)
# init luminance importance map
importanceBaselineLuminance.setTestFile(dataset_file)
if importanceBaselineLuminance.isAvailable():
print("Luminance-contrast importance map is available")
# create output file
os.makedirs(OUTPUT_FOLDER, exist_ok=True)
output_file = os.path.join(OUTPUT_FOLDER, dataset_name + '.hdf5')
print("Save to", output_file)
with h5py.File(output_file, 'a') as output_hdf5_file:
# load dataset
set = FullResDataset(dataset_file)
data_loader = torch.utils.data.DataLoader(set,
batch_size=BATCH_SIZE,
shuffle=False)
# define statistics
StatsCfg = collections.namedtuple(
"StatsCfg", "stats importance recon heatmin heatmean pattern")
statistics = []
for (inet, rnet) in NETWORK_COMBINATIONS:
for (heatmin, heatmean) in HEATMAP_CFG:
for pattern in SAMPLING_PATTERNS:
stats_info = {
'importance': inet.name(),
'reconstruction': rnet.name(),
'heatmin': heatmin,
'heatmean': heatmean,
'pattern': pattern
}
stats_filename = "Stats_%s_%s_%03d_%03d_%s" % (
inet.name(), rnet.name(), heatmin * 100,
heatmean * 100, pattern)
histo_filename = "Histogram_%s_%s_%03d_%03d_%s" % (
inet.name(), rnet.name(), heatmin * 100,
heatmean * 100, pattern)
s = Statistics()
s.create_datasets(output_hdf5_file, stats_filename,
histo_filename,
len(set) * set.num_timesteps(),
stats_info)
statistics.append(
StatsCfg(stats=s,
importance=inet,
recon=rnet,
heatmin=heatmin,
heatmean=heatmean,
pattern=pattern))
print(len(statistics),
" different combinations are performed per sample")
# compute statistics
try:
with torch.no_grad():
num_minibatch = len(data_loader)
pg = ProgressBar(num_minibatch, 'Evaluation', length=50)
for iteration, (batch, batch_indices) in enumerate(
data_loader, 0):
pg.print_progress_bar(iteration)
batch = batch.to(device)
importanceBaselineLuminance.setIndices(batch_indices)
B, T, C, H, W = batch.shape
# try out each combination
for s in statistics:
#print(s)
# get input to evaluation
importanceNetUpscale = s.importance.networkUpscaling(
)
importancePostUpscale = UPSCALING // importanceNetUpscale
crop_low = torch.nn.functional.interpolate(
batch.reshape(B * T, C, H, W),
scale_factor=1 / UPSCALING,
mode='area').reshape(B, T, C, H // UPSCALING,
W // UPSCALING)
pattern = sampling_pattern[s.pattern][:H, :W]
crop_high = batch
# loop over timesteps
pattern = pattern.unsqueeze(0).unsqueeze(0)
previous_importance = None
previous_output = None
reconstructions = []
for j in range(T):
importanceBaselineLuminance.setTime(j)
# extract flow (always the last two channels of crop_high)
flow = crop_high[:, j, C - 2:, :, :]
# compute importance map
importance_input = crop_low[:, j, :5, :, :]
if j == 0 or s.importance.disableTemporal:
previous_input = torch.zeros(
B,
1,
importance_input.shape[2] *
importanceNetUpscale,
importance_input.shape[3] *
importanceNetUpscale,
dtype=crop_high.dtype,
device=crop_high.device)
else:
flow_low = F.interpolate(
flow,
scale_factor=1 / importancePostUpscale)
previous_input = models.VideoTools.warp_upscale(
previous_importance, flow_low, 1,
False)
importance_map = s.importance.call(
importance_input, previous_input)
if len(importance_map.shape) == 3:
importance_map = importance_map.unsqueeze(
1)
previous_importance = importance_map
target_mean = s.heatmean
if USE_BINARY_SEARCH_ON_MEAN:
# For regular sampling, the normalization does not work properly,
# use binary search on the heatmean instead
def f(x):
postprocess = importance.PostProcess(
s.heatmin, x,
importancePostUpscale,
LOSS_BORDER //
importancePostUpscale, 'basic')
importance_map2 = postprocess(
importance_map)[0].unsqueeze(1)
sampling_mask = (
importance_map2 >= pattern).to(
dtype=importance_map.dtype)
samples = torch.mean(
sampling_mask).item()
return samples
target_mean = binarySearch(
f, s.heatmean, s.heatmean, 10, 0, 1)
#print("Binary search for #samples, mean start={}, result={} with samples={}, original={}".
# format(s.heatmean, s.heatmean, f(target_mean), f(s.heatmean)))
# normalize and upscale importance map
postprocess = importance.PostProcess(
s.heatmin, target_mean,
importancePostUpscale,
LOSS_BORDER // importancePostUpscale,
'basic')
importance_map = postprocess(
importance_map)[0].unsqueeze(1)
#print("mean:", torch.mean(importance_map).item())
# create samples
sample_mask = (importance_map >= pattern).to(
dtype=importance_map.dtype)
reconstruction_input = torch.cat(
(
sample_mask *
crop_high[:, j, 0:
5, :, :], # mask, normal x, normal y, normal z, depth
sample_mask * torch.ones(
B,
1,
H,
W,
dtype=crop_high.dtype,
device=crop_high.device), # ao
sample_mask), # sample mask
dim=1)
# warp previous output
if j == 0 or s.recon.disableTemporal:
previous_input = torch.zeros(
B,
6,
H,
W,
dtype=crop_high.dtype,
device=crop_high.device)
else:
previous_input = models.VideoTools.warp_upscale(
previous_output, flow, 1, False)
# run reconstruction network
reconstruction = s.recon.call(
reconstruction_input, sample_mask,
previous_input)
# clamp
reconstruction_clamped = torch.cat(
[
torch.clamp(
reconstruction[:, 0:1, :, :], -1,
+1), # mask
ScreenSpaceShading.normalize(
reconstruction[:, 1:4, :, :],
dim=1),
torch.clamp(
reconstruction[:, 4:5, :, :], 0,
+1), # depth
torch.clamp(reconstruction[:,
5:6, :, :],
0, +1) # ao
],
dim=1)
reconstructions.append(reconstruction_clamped)
# save for next frame
previous_output = reconstruction_clamped
#endfor: timesteps
# compute statistics
reconstructions = torch.cat(reconstructions, dim=0)
crops_high = torch.cat(
[crop_high[:, j, :6, :, :] for j in range(T)],
dim=0)
sample_masks = torch.cat([sample_mask] * T, dim=0)
s.stats.add_timestep_sample(
reconstructions, crops_high, sample_masks)
# endfor: statistic
# endfor: batch
pg.print_progress_bar(num_minibatch)
# end no_grad()
finally:
# close files
for s in statistics:
s.stats.write_histogram()
s.stats.close_stats_file()
class LossNetSparse(nn.Module):
"""
Main Loss Module for unshaded data.
device: cpu or cuda
losses: list of loss terms with weighting as string
Format: <loss>:<target>:<weighting>
with: loss in {l1, l2, tl2}
target in {mask, normal, color, ao}
weighting a positive number
opt: command line arguments (Namespace object) with:
- further parameters depending on the losses
"""
def __init__(self, device, channels, losses, opt, has_flow):
super().__init__()
self.padding = opt.lossBorderPadding
self.has_flow = has_flow
if has_flow:
assert channels == 8 # mask, normalX, normalY, normalZ, depth, ao, flowX, floatY
else:
assert channels == 6 # mask, normalX, normalY, normalZ, depth, ao
#List of tuples (name, weight or None)
self.loss_list = [s.split(':') for s in losses.split(',')]
# Build losses and weights
builder = LossBuilder(device)
self.loss_dict = {}
self.weight_dict = {}
self.loss_dict['mse'] = builder.mse() #always use mse for psnr
self.weight_dict[('mse', 'color')] = 0.0
TARGET_INFO = {
# target name : (#channels, expected min, expected max)
"mask": (1, -1, +1),
"normal": (3, -1, +1),
"color": (3, 0, 1),
"ao": (1, 0, 1),
"depth": (1, 0, 1)
}
content_layers = []
style_layers = []
self.has_discriminator = False
self.has_style_or_content_loss = False
self.has_temporal_l2_loss = False
for entry in self.loss_list:
if len(entry) < 2:
raise ValueError("illegal format for loss list: " + entry)
name = entry[0]
target = entry[1]
weight = entry[2] if len(entry) > 2 else None
if target != 'mask' and target != 'normal' and target != 'color' and target != 'ao' and target != 'depth' and target != 'flow':
raise ValueError("Unknown target: " + target)
if 'mse' == name or 'l2' == name or 'l2_loss' == name:
self.weight_dict[(
'mse',
target)] = float(weight) if weight is not None else 1.0
elif 'l1' == name or 'l1_loss' == name:
self.loss_dict['l1'] = builder.l1_loss()
self.weight_dict[(
'l1',
target)] = float(weight) if weight is not None else 1.0
elif 'tl2' == name or 'temp-l2' == name:
self.loss_dict['temp-l2'] = builder.mse()
self.weight_dict[(
'temp-l2',
target)] = float(weight) if weight is not None else 1.0
self.has_temporal_l2_loss = True
elif 'tl1' == name or 'temp-l1' == name:
self.loss_dict['temp-l1'] = builder.l1_loss()
self.weight_dict[(
'temp-l1',
target)] = float(weight) if weight is not None else 1.0
self.has_temporal_l2_loss = True
elif 'bounded' == name:
if target != 'mask':
raise ValueError(
"'bounded' loss can only be applied on the mask")
self.weight_dict[(
"bounded",
"mask")] = float(weight) if weight is not None else 1.0
elif 'bce' == name:
if target != 'mask':
raise ValueError(
"'bce' loss can only be applied on the mask")
self.weight_dict[(
"bce",
"mask")] = float(weight) if weight is not None else 1.0
elif 'ssim' == name:
self.loss_dict['ssim'] = builder.ssim_loss(
TARGET_INFO[target][0])
self.weight_dict[(
'ssim',
target)] = float(weight) if weight is not None else 1.0
elif 'dssim' == name:
self.loss_dict['dssim'] = builder.dssim_loss(
TARGET_INFO[target][0])
self.weight_dict[(
'dssim',
target)] = float(weight) if weight is not None else 1.0
elif 'lpips' == name:
self.loss_dict['lpips'] = builder.lpips_loss(
*TARGET_INFO[target])
self.weight_dict[(
'lpips',
target)] = float(weight) if weight is not None else 1.0
else:
raise ValueError('unknown loss %s' % name)
self.loss_dict = nn.ModuleDict(self.loss_dict)
print('Loss weights:', self.weight_dict)
self.shading = ScreenSpaceShading(device)
self.shading.fov(30)
self.shading.ambient_light_color(
np.array([opt.lossAmbient, opt.lossAmbient, opt.lossAmbient]))
self.shading.diffuse_light_color(
np.array([opt.lossDiffuse, opt.lossDiffuse, opt.lossDiffuse]))
self.shading.specular_light_color(
np.array([opt.lossSpecular, opt.lossSpecular, opt.lossSpecular]))
self.shading.specular_exponent(16)
self.shading.enable_specular = False
self.shading.light_direction(np.array([0.0, 0.0, 1.0]))
self.shading.material_color(np.array([1.0, 1.0, 1.0]))
self.shading.ambient_occlusion(opt.lossAO)
print('LossNet: ambient occlusion strength:', opt.lossAO)
@staticmethod
def pad(img, border):
"""
overwrites the border of 'img' with zeros.
The size of the border is specified by 'border'.
The output size is not changed.
"""
if border == 0:
return img
b, c, h, w = img.shape
img_crop = img[:, :, border:h - border, border:h - border]
img_pad = F.pad(img_crop, (border, border, border, border), 'constant',
0)
_, _, h2, w2 = img_pad.shape
assert (h == h2)
assert (w == w2)
return img_pad
#@profile
def forward(self,
gt,
pred,
prev_pred_warped,
no_temporal_loss: bool,
use_checkpoints=False):
"""
gt: ground truth high resolution image (B x C x H x W)
pred: predicted high resolution image (B x C x H x W)
prev_pred_warped: predicted image from the previous frame warped by the flow
Shape: B x C x H x W
Only used for temporal losses, can be None if only the other losses are used
use_checkpoints: True if checkpointing should be used.
This does not apply here since we don't have GANs or perceptual losses
"""
# TODO: loss that penalizes deviation from the input samples
B, C, H, W = gt.shape
if self.has_flow:
assert C == 8
else:
assert C == 6
assert gt.shape == pred.shape
# zero border padding
gt = LossNetSparse.pad(gt, self.padding)
pred = LossNetSparse.pad(pred, self.padding)
if prev_pred_warped is not None:
prev_pred_warped = LossNetSparse.pad(prev_pred_warped,
self.padding)
# extract mask and normal
gt_mask = gt[:, 0:1, :, :]
gt_mask_clamp = torch.clamp(gt_mask * 0.5 + 0.5, 0, 1)
gt_normal = ScreenSpaceShading.normalize(gt[:, 1:4, :, :], dim=1)
gt_depth = gt[:, 4:5, :, :]
gt_ao = gt[:, 5:6, :, :]
if self.has_flow:
gt_flow = gt[:, 6:8, :, :]
pred_mask = pred[:, 0:1, :, :]
pred_mask_clamp = torch.clamp(pred_mask * 0.5 + 0.5, 0, 1)
pred_normal = ScreenSpaceShading.normalize(pred[:, 1:4, :, :], dim=1)
pred_depth = pred[:, 4:5, :, :]
pred_ao = pred[:, 5:6, :, :]
if self.has_flow:
pred_flow = pred[:, 6:8, :, :]
if prev_pred_warped is not None and self.has_temporal_l2_loss:
prev_pred_mask = prev_pred_warped[:, 0:1, :, :]
prev_pred_mask_clamp = torch.clamp(prev_pred_mask * 0.5 + 0.5, 0,
1)
prev_pred_normal = ScreenSpaceShading.normalize(
prev_pred_warped[:, 1:4, :, :], dim=1)
prev_pred_depth = prev_pred_warped[:, 4:5, :, :]
prev_pred_ao = prev_pred_warped[:, 5:6, :, :]
if self.has_flow:
prev_pred_flow = prev_pred_warped[:, 6:8, :, :]
generator_loss = 0.0
loss_values = {}
# normal, simple losses, uses gt+pred
for name in ['mse', 'l1', 'ssim', 'dssim', 'lpips']:
if (name, 'mask') in self.weight_dict.keys():
loss = self.loss_dict[name](gt_mask, pred_mask)
loss_values[(name, 'mask')] = loss.item()
generator_loss += self.weight_dict[(name, 'mask')] * loss
if (name, 'normal') in self.weight_dict.keys():
loss = self.loss_dict[name](gt_normal * gt_mask_clamp,
pred_normal * gt_mask_clamp)
if torch.isnan(loss).item():
test = self.loss_dict[name](gt_normal * gt_mask_clamp,
pred_normal * gt_mask_clamp)
loss_values[(name, 'normal')] = loss.item()
generator_loss += self.weight_dict[(name, 'normal')] * loss
if (name, 'ao') in self.weight_dict.keys():
loss = self.loss_dict[name](gt_ao * gt_mask_clamp,
pred_ao * gt_mask_clamp)
loss_values[(name, 'ao')] = loss.item()
generator_loss += self.weight_dict[(name, 'ao')] * loss
if (name, 'depth') in self.weight_dict.keys():
loss = self.loss_dict[name](gt_depth * gt_mask_clamp,
pred_depth * gt_mask_clamp)
loss_values[(name, 'depth')] = loss.item()
generator_loss += self.weight_dict[(name, 'depth')] * loss
if (name, 'flow') in self.weight_dict.keys():
# note: flow is not restricted to inside regions
loss = self.loss_dict[name](gt_flow, pred_flow)
loss_values[(name, 'flow')] = loss.item()
generator_loss += self.weight_dict[(name, 'flow')] * loss
if (name, 'color') in self.weight_dict.keys():
gt_color = self.shading(gt)
pred_color = self.shading(pred)
loss = self.loss_dict[name](gt_color, pred_color)
loss_values[(name, 'color')] = loss.item()
generator_loss += self.weight_dict[(name, 'color')] * loss
if ("bounded", "mask") in self.weight_dict.keys():
# penalizes if the mask diverges too far away from [0,1]
zero = torch.zeros(1,
1,
1,
1,
dtype=pred_mask.dtype,
device=pred_mask.device)
loss = torch.mean(torch.max(zero, pred_mask * pred_mask - 2))
loss_values[("bounded", "mask")] = loss.item()
generator_loss += self.weight_dict[("bounded", "mask")] * loss
if ("bce", "mask") in self.weight_dict.keys():
# binary cross entry loss between the unclamped masks
loss = F.binary_cross_entropy_with_logits(pred_mask * 0.5 + 0.5,
gt_mask * 0.5 + 0.5)
loss_values[("bce", "mask")] = loss.item()
generator_loss += self.weight_dict[("bce", "mask")] * loss
# temporal l2 loss, uses input (for the mask) + pred + prev_warped
if self.has_temporal_l2_loss and not no_temporal_loss:
assert prev_pred_warped is not None
for name in ['temp-l2', 'temp-l1']:
if (name, 'mask') in self.weight_dict.keys():
loss = self.loss_dict[name](pred_mask, prev_pred_mask)
loss_values[(name, 'mask')] = loss.item()
generator_loss += self.weight_dict[(name, 'mask')] * loss
if (name, 'normal') in self.weight_dict.keys():
loss = self.loss_dict[name](pred_normal * gt_mask_clamp,
prev_pred_normal *
gt_mask_clamp)
loss_values[(name, 'normal')] = loss.item()
generator_loss += self.weight_dict[(name, 'normal')] * loss
if (name, 'ao') in self.weight_dict.keys():
prev_pred_ao = prev_pred_warped[:, 5:6, :, :]
loss = self.loss_dict[name](pred_ao * gt_mask_clamp,
prev_pred_ao * gt_mask_clamp)
loss_values[(name, 'ao')] = loss.item()
generator_loss += self.weight_dict[(name, 'ao')] * loss
if (name, 'depth') in self.weight_dict.keys():
prev_pred_depth = prev_pred_warped[:, 4:5, :, :]
loss = self.loss_dict[name](pred_depth * gt_mask_clamp,
prev_pred_depth *
gt_mask_clamp)
loss_values[(name, 'depth')] = loss.item()
generator_loss += self.weight_dict[(name, 'depth')] * loss
if (name, 'flow') in self.weight_dict.keys():
prev_pred_depth = prev_pred_warped[:, 4:5, :, :]
loss = self.loss_dict[name](
pred_flow, #note: no restriction to inside areas
prev_pred_flow)
loss_values[(name, 'flow')] = loss.item()
generator_loss += self.weight_dict[(name, 'flow')] * loss
if (name, 'color') in self.weight_dict.keys():
prev_pred_color = self.shading(prev_pred_warped)
loss = self.loss_dict[name](pred_color, prev_pred_color)
loss_values[(name, 'color')] = loss.item()
generator_loss += self.weight_dict[(name, 'color')] * loss
return generator_loss, loss_values
class GUI(tk.Tk):
MODE_LOW = 1
MODE_HIGH = 2
MODE_FLOW = 3
CHANNEL_MASK = 1
CHANNEL_NORMAL = 2
CHANNEL_DEPTH = 3
CHANNEL_AO = 4
CHANNEL_COLOR = 5
def __init__(self):
tk.Tk.__init__(self)
self.title('Dataset Viewer')
self.input_name = None
# members to be filled later
self.pilImage = None
self.tikImage = None
self.current_high = None
self.current_low = None
self.current_flow = None
self.dataset_folder = None
self.last_folder = INITIAL_DIR
self.entries = []
self.num_frames = 0
self.selected_time = 0
# root
self.root_panel = tk.Frame(self)
self.root_panel.pack(side="bottom", fill="both", expand="yes")
self.panel = tk.Label(self.root_panel)
self.black = np.zeros((512, 512, 3))
self.setImage(self.black)
self.panel.pack(side="left", fill="both", expand="yes")
options1 = tk.Label(self.root_panel)
# Input
inputFrame = ttk.LabelFrame(options1, text="Input", relief=tk.RIDGE)
tk.Button(inputFrame,
text="Open Folder",
command=lambda: self.openFolder()).pack()
self.dataset_entry = 0
self.dataset_entry_slider = tk.Scale(
inputFrame,
from_=0,
to=0,
orient=tk.HORIZONTAL,
resolution=1,
label="Entry",
showvalue=0,
command=lambda e: self.setEntry(int(e)))
self.dataset_entry_slider.pack(anchor=tk.W, fill=tk.X)
self.dataset_time = 0
self.dataset_time_slider = tk.Scale(
inputFrame,
from_=0,
to=0,
orient=tk.HORIZONTAL,
resolution=1,
label="Time",
showvalue=0,
command=lambda e: self.setTime(int(e)))
self.dataset_time_slider.pack(anchor=tk.W, fill=tk.X)
inputFrame.pack(fill=tk.X)
# Mode
modeFrame = ttk.LabelFrame(options1, text="Mode", relief=tk.RIDGE)
self.mode = tk.IntVar()
self.mode.set(GUI.MODE_LOW)
self.mode.trace_add('write', lambda a, b, c: self.updateImage())
tk.Radiobutton(modeFrame,
text="Low",
variable=self.mode,
value=GUI.MODE_LOW).pack(anchor=tk.W)
tk.Radiobutton(modeFrame,
text="High",
variable=self.mode,
value=GUI.MODE_HIGH).pack(anchor=tk.W)
tk.Radiobutton(modeFrame,
text="Flow",
variable=self.mode,
value=GUI.MODE_FLOW).pack(anchor=tk.W)
modeFrame.pack(fill=tk.X)
# Channel
channelsFrame = ttk.LabelFrame(options1,
text="Channel",
relief=tk.RIDGE)
self.channel_mode = tk.IntVar()
self.channel_mode.set(GUI.CHANNEL_COLOR)
self.channel_mode.trace_add('write',
lambda a, b, c: self.updateImage())
tk.Radiobutton(channelsFrame,
text="Mask",
variable=self.channel_mode,
value=GUI.CHANNEL_MASK).pack(anchor=tk.W)
tk.Radiobutton(channelsFrame,
text="Normal",
variable=self.channel_mode,
value=GUI.CHANNEL_NORMAL).pack(anchor=tk.W)
tk.Radiobutton(channelsFrame,
text="Depth",
variable=self.channel_mode,
value=GUI.CHANNEL_DEPTH).pack(anchor=tk.W)
tk.Radiobutton(channelsFrame,
text="AO",
variable=self.channel_mode,
value=GUI.CHANNEL_AO).pack(anchor=tk.W)
tk.Radiobutton(channelsFrame,
text="Color",
variable=self.channel_mode,
value=GUI.CHANNEL_COLOR).pack(anchor=tk.W)
channelsFrame.pack(fill=tk.X)
# Shading
self.shading = ScreenSpaceShading('cpu')
self.shading.fov(30)
self.ambient_light_color = np.array([0.1, 0.1, 0.1])
self.shading.ambient_light_color(self.ambient_light_color)
self.diffuse_light_color = np.array([0.8, 0.8, 0.8])
self.shading.diffuse_light_color(self.diffuse_light_color)
self.specular_light_color = np.array([0.02, 0.02, 0.02])
self.shading.specular_light_color(self.specular_light_color)
self.shading.specular_exponent(16)
self.shading.light_direction(np.array([0.1, 0.1, 1.0]))
self.material_color = np.array([1.0, 1.0, 1.0]) #[1.0, 0.3, 0.0])
self.shading.material_color(self.material_color)
self.shading.ambient_occlusion(0.5)
self.shading.background(np.array([1.0, 1.0, 1.0]))
# Save
tk.Button(options1,
text="Save Image",
command=lambda: self.saveImage()).pack()
self.saveFolder = "/"
options1.pack(side="left")
def openFolder(self):
dataset_folder = filedialog.askdirectory(initialdir=self.last_folder)
print(dataset_folder)
if dataset_folder is not None:
self.dataset_folder = str(dataset_folder)
print("New folder selected:", self.dataset_folder)
self.last_folder = self.dataset_folder
# find number of entries
self.current_low = None
self.current_high = None
self.current_flow = None
entries = []
for i in range(0, 10000):
file_low = os.path.join(self.dataset_folder,
"low_%05d.npy" % i)
if os.path.isfile(file_low):
entries.append((file_low,
os.path.join(self.dataset_folder,
"high_%05d.npy" % i),
os.path.join(self.dataset_folder,
"flow_%05d.npy" % i)))
else:
break
print("Number of entries found:", len(entries))
self.entries = entries
self.dataset_entry = 0
self.dataset_entry_slider.config(to=len(entries))
self.setEntry(0)
else:
print("No folder selected")
def setEntry(self, entry):
entry = min(entry, len(self.entries) - 1)
self.dataset_entry_slider.config(label='Entry: %d' % int(entry))
if len(self.entries) == 0:
self.current_low = None
self.current_high = None
self.current_flow = None
else:
self.current_low = np.load(self.entries[entry][0])
self.current_high = np.load(self.entries[entry][1])
self.current_flow = np.load(self.entries[entry][2])
self.num_frames = self.current_low.shape[0]
self.dataset_time_slider.config(to=self.num_frames)
print("Entry loaded")
self.setTime(0)
def setTime(self, entry):
entry = min(entry, self.num_frames - 1)
self.dataset_time_slider.config(label='Time: %d' % int(entry))
self.selected_time = entry
self.updateImage()
def setImage(self, img):
self.pilImage = pl.Image.fromarray(
np.clip((img * 255).transpose((1, 0, 2)), 0, 255).astype(np.uint8))
self.tikImage = ImageTk.PhotoImage(self.pilImage)
self.panel.configure(image=self.tikImage)
def saveImage(self):
filename = filedialog.asksaveasfilename(
initialdir=self.saveFolder,
title="Save as",
filetypes=(("jpeg files", "*.jpg"), ("png files", "*.png"),
("all files", "*.*")))
if len(filename) == 0:
return
if len(os.path.splitext(filename)[1]) == 0:
filename = filename + ".jpg"
self.pilImage.save(filename)
self.saveFolder = os.path.dirname(filename)
def updateImage(self):
if self.current_low is None:
# no image loaded
self.setImage(self.black)
return
def selectChannel(img):
if self.channel_mode.get() == GUI.CHANNEL_MASK:
mask = img[0:1, :, :] * 0.5 + 0.5
return np.concatenate((mask, mask, mask))
elif self.channel_mode.get() == GUI.CHANNEL_NORMAL:
return img[1:4, :, :] * 0.5 + 0.5
elif self.channel_mode.get() == GUI.CHANNEL_DEPTH:
return np.concatenate(
(img[4:5, :, :], img[4:5, :, :], img[4:5, :, :]))
elif self.channel_mode.get() == GUI.CHANNEL_AO:
if img.shape[0] == 6:
return np.concatenate(
(img[5:6, :, :], img[5:6, :, :], img[5:6, :, :]))
else:
return np.zeros((3, img.shape[1], img.shape[2]),
dtype=np.float32)
elif self.channel_mode.get() == GUI.CHANNEL_COLOR:
shading_input = torch.unsqueeze(torch.from_numpy(img), 0)
shading_output = self.shading(shading_input)[0]
return torch.clamp(shading_output, 0, 1).cpu().numpy()
if self.mode.get() == GUI.MODE_LOW:
img = self.current_low[self.selected_time, :, :, :]
img = selectChannel(img)
img = img.transpose((2, 1, 0))
img = cv.resize(img,
dsize=None,
fx=UPSCALE,
fy=UPSCALE,
interpolation=cv.INTER_NEAREST)
self.setImage(img)
elif self.mode.get() == GUI.MODE_HIGH:
img = self.current_high[self.selected_time, :, :, :]
img = selectChannel(img)
img = img.transpose((2, 1, 0))
self.setImage(img)
elif self.mode.get() == GUI.MODE_FLOW:
#img = np.stack((
# cv.inpaint(self.current_flow[self.selected_time,0,:,:], np.uint8(self.current_low[self.selected_time,0,:,:]==0), 3, cv.INPAINT_NS),
# cv.inpaint(self.current_flow[self.selected_time,1,:,:], np.uint8(self.current_low[self.selected_time,0,:,:]==0), 3, cv.INPAINT_NS),
# np.zeros((self.current_flow.shape[2], self.current_flow.shape[3]))), axis=0).astype(np.float32)
img = np.concatenate(
(self.current_flow[self.selected_time, 0:2, :, :],
np.zeros((1, self.current_flow.shape[2],
self.current_flow.shape[3]),
dtype=np.float32)), )
img = (img * 10 + 0.5)
img = img.transpose((2, 1, 0))
img = cv.resize(img,
dsize=None,
fx=UPSCALE,
fy=UPSCALE,
interpolation=cv.INTER_NEAREST)
self.setImage(img)
class GUI(tk.Tk):
CHANNEL_MASK = 1
CHANNEL_NORMAL = 2
CHANNEL_DEPTH = 3
CHANNEL_AO = 4
CHANNEL_COLOR = 5
CHANNEL_FLOW = 6
def __init__(self):
tk.Tk.__init__(self)
self.title('Dataset Viewer')
self.input_name = None
# members to be filled later
self.pilImage = None
self.tikImage = None
self.num_frames = 0
self.selected_entry = 0
self.selected_time = 0
self.last_folder = None
self.dataset_file = None
self.hdf5_file = None
self.dset_keys = []
self.dset = None
self.mode = None
# root
self.root_panel = tk.Frame(self)
self.root_panel.pack(side="bottom", fill="both", expand="yes")
self.panel = tk.Label(self.root_panel)
self.black = np.zeros((512, 512, 3))
self.setImage(self.black)
self.panel.pack(side="left", fill="both", expand="yes")
options1 = tk.Label(self.root_panel)
# Input
inputFrame = ttk.LabelFrame(options1, text="Input", relief=tk.RIDGE)
tk.Button(inputFrame,
text="Open HDF5",
command=lambda: self.openHDF5()).pack()
listbox_frame = tk.Frame(inputFrame)
self.dset_listbox_scrollbar = tk.Scrollbar(listbox_frame,
orient=tk.VERTICAL)
self.dset_listbox = tk.Listbox(
listbox_frame,
selectmode=tk.SINGLE,
yscrollcommand=self.dset_listbox_scrollbar.set)
self.dset_listbox_scrollbar.config(command=self.dset_listbox.yview)
self.dset_listbox_scrollbar.pack(side=tk.RIGHT, fill=tk.Y)
self.dset_listbox.pack(side=tk.LEFT, anchor=tk.W, fill=tk.X, expand=1)
self.dset_listbox.bind("<Double-Button-1>", self.setDsetCallback)
listbox_frame.pack(anchor=tk.W, fill=tk.X)
self.selected_dset = None
self.dataset_entry_slider = tk.Scale(
inputFrame,
from_=0,
to=0,
orient=tk.HORIZONTAL,
resolution=1,
label="Entry",
showvalue=0,
command=lambda e: self.setEntry(int(e)))
self.dataset_entry_slider.pack(anchor=tk.W, fill=tk.X)
self.dataset_time = 0
self.dataset_time_slider = tk.Scale(
inputFrame,
from_=0,
to=0,
orient=tk.HORIZONTAL,
resolution=1,
label="Time",
showvalue=0,
command=lambda e: self.setTime(int(e)))
self.dataset_time_slider.pack(anchor=tk.W, fill=tk.X)
inputFrame.pack(fill=tk.X)
# Channel
channelsFrame = ttk.LabelFrame(options1,
text="Channel",
relief=tk.RIDGE)
self.channel_mode = tk.IntVar()
self.channel_mode.set(GUI.CHANNEL_COLOR)
self.channel_mode.trace_add('write',
lambda a, b, c: self.updateImage())
tk.Radiobutton(channelsFrame,
text="Mask",
variable=self.channel_mode,
value=GUI.CHANNEL_MASK).pack(anchor=tk.W)
tk.Radiobutton(channelsFrame,
text="Normal",
variable=self.channel_mode,
value=GUI.CHANNEL_NORMAL).pack(anchor=tk.W)
tk.Radiobutton(channelsFrame,
text="Depth",
variable=self.channel_mode,
value=GUI.CHANNEL_DEPTH).pack(anchor=tk.W)
tk.Radiobutton(channelsFrame,
text="AO",
variable=self.channel_mode,
value=GUI.CHANNEL_AO).pack(anchor=tk.W)
tk.Radiobutton(channelsFrame,
text="Color",
variable=self.channel_mode,
value=GUI.CHANNEL_COLOR).pack(anchor=tk.W)
tk.Radiobutton(channelsFrame,
text="Flow",
variable=self.channel_mode,
value=GUI.CHANNEL_FLOW).pack(anchor=tk.W)
channelsFrame.pack(fill=tk.X)
# Shading
self.shading = ScreenSpaceShading('cpu')
self.shading.fov(30)
self.ambient_light_color = np.array([0.1, 0.1, 0.1])
self.shading.ambient_light_color(self.ambient_light_color)
self.diffuse_light_color = np.array([0.8, 0.8, 0.8])
self.shading.diffuse_light_color(self.diffuse_light_color)
self.specular_light_color = np.array([0.02, 0.02, 0.02])
self.shading.specular_light_color(self.specular_light_color)
self.shading.specular_exponent(16)
self.shading.light_direction(np.array([0.1, 0.1, 1.0]))
self.material_color = np.array([1.0, 1.0, 1.0]) #[1.0, 0.3, 0.0])
self.shading.material_color(self.material_color)
self.shading.ambient_occlusion(0.5)
self.shading.background(np.array([1.0, 1.0, 1.0]))
# Save
tk.Button(options1,
text="Save Image",
command=lambda: self.saveImage()).pack()
self.saveFolder = "/"
options1.pack(side="left")
def openHDF5(self):
dataset_file = filedialog.askopenfilename(initialdir=self.last_folder,
title="Select HDF5 file",
filetypes=(("HDF5 files",
"*.hdf5"), ))
print(dataset_file)
if dataset_file is not None:
self.dataset_file = str(dataset_file)
print("New hdf5 selected:", self.dataset_file)
self.title(self.dataset_file)
self.last_folder = os.path.dirname(self.dataset_file)
# load hdf5 file
self.hdf5_file = h5py.File(dataset_file, "r")
# list datasets
self.dset_listbox.delete(0, tk.END)
self.dset_keys = list(self.hdf5_file.keys())
for key in self.dset_keys:
self.dset_listbox.insert(tk.END, key)
self.dset_listbox.selection_set(first=0)
self.setDset(self.dset_keys[0])
else:
print("No folder selected")
def setDsetCallback(self, *args):
items = self.dset_listbox.curselection()
items = [self.dset_keys[int(item)] for item in items]
self.setDset(items[0])
def setDset(self, name):
self.selected_dset = name
print("Select dataset '%s'" % (self.selected_dset))
self.dset = self.hdf5_file[self.selected_dset]
self.mode = self.dset.attrs.get("Mode", "IsoUnshaded")
print("Mode:", self.mode)
# find number of entries
entries = self.dset.shape[0]
num_frames = self.dset.shape[1]
print("Number of entries found:", entries, "with", num_frames,
"timesteps")
print("Image size:", self.dset.shape[3], "x", self.dset.shape[4])
self.entries = entries
self.num_frames = num_frames
self.dataset_entry_slider.config(to=entries - 1)
self.dataset_time_slider.config(to=num_frames - 1)
self.setEntry(
self.selected_entry if self.selected_entry < entries else 0)
def setEntry(self, entry):
entry = min(entry, self.entries - 1)
self.dataset_entry_slider.config(label='Entry: %d' % int(entry))
self.selected_entry = entry
self.updateImage()
def setTime(self, entry):
entry = min(entry, self.num_frames - 1)
self.dataset_time_slider.config(label='Time: %d' % int(entry))
self.selected_time = entry
self.updateImage()
def setImage(self, img):
if img.dtype == np.uint8:
self.pilImage = pl.Image.fromarray(img.transpose((1, 0, 2)))
else:
self.pilImage = pl.Image.fromarray(
np.clip((img * 255).transpose((1, 0, 2)), 0,
255).astype(np.uint8))
if self.pilImage.size[0] <= 256:
self.pilImage = self.pilImage.resize(
(self.pilImage.size[0] * 2, self.pilImage.size[1] * 2),
pl.Image.NEAREST)
self.tikImage = ImageTk.PhotoImage(self.pilImage)
self.panel.configure(image=self.tikImage)
def saveImage(self):
filename = filedialog.asksaveasfilename(
initialdir=self.saveFolder,
title="Save as",
filetypes=(("png files", "*.png"), ("jpeg files", "*.jpg"),
("all files", "*.*")))
if len(filename) == 0:
return
if len(os.path.splitext(filename)[1]) == 0:
filename = filename + ".png"
self.pilImage.save(filename)
self.saveFolder = os.path.dirname(filename)
def updateImage(self):
if self.dset is None:
# no image loaded
self.setImage(self.black)
return
def selectChannel(img):
if self.mode == "DVR":
mask = img[3:4, :, :]
if self.channel_mode.get() == GUI.CHANNEL_MASK:
return np.concatenate((mask, mask, mask))
elif self.channel_mode.get() == GUI.CHANNEL_COLOR:
return img[0:3, :, :]
elif self.channel_mode.get() == GUI.CHANNEL_NORMAL:
return (img[4:7, :, :] * 0.5 + 0.5) * mask
elif self.channel_mode.get() == GUI.CHANNEL_DEPTH:
return np.concatenate([img[7:8, :, :]] * 3, axis=0)
elif self.channel_mode.get() == GUI.CHANNEL_FLOW:
return np.concatenate(
(img[8:9, :, :] * 10, img[9:10, :, :] * 10,
np.zeros_like(img[6:7, :, :]))) + 0.5
else:
return self.black
else: # IsoUnshaded
if self.channel_mode.get() == GUI.CHANNEL_MASK:
if img.dtype == np.uint8:
mask = img[0:1, :, :]
else:
mask = img[0:1, :, :] * 0.5 + 0.5
return np.concatenate((mask, mask, mask))
elif self.channel_mode.get() == GUI.CHANNEL_NORMAL:
if img.dtype == np.uint8:
return img[1:4, :, :]
else:
return img[1:4, :, :] * 0.5 + 0.5
elif self.channel_mode.get() == GUI.CHANNEL_DEPTH:
return np.concatenate(
(img[4:5, :, :], img[4:5, :, :], img[4:5, :, :]))
elif self.channel_mode.get() == GUI.CHANNEL_AO:
return np.concatenate(
(img[5:6, :, :], img[5:6, :, :], img[5:6, :, :]))
elif self.channel_mode.get() == GUI.CHANNEL_FLOW:
return np.concatenate(
(img[6:7, :, :] * 10, img[7:8, :, :] * 10,
np.zeros_like(img[6:7, :, :]))) + 0.5
elif self.channel_mode.get() == GUI.CHANNEL_COLOR:
if img.dtype == np.uint8:
shading_input = torch.unsqueeze(
torch.from_numpy(img.astype(np.float32) / 255.0),
0)
shading_input[:, 1:
4, :, :] = shading_input[:, 1:
4, :, :] * 2 - 1
else:
shading_input = torch.unsqueeze(
torch.from_numpy(img), 0)
shading_output = self.shading(shading_input)[0]
return torch.clamp(shading_output, 0, 1).cpu().numpy()
img = self.dset[self.selected_entry, self.selected_time, :, :, :]
img = selectChannel(img)
img = img.transpose((2, 1, 0))
self.setImage(img)