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optics.py
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import os
import copy
import torch
import RefraSurfs
import targetPlane
import imgTools
import util
import visTools
import pointArrays
import pytorch_lightning as pl
from torchmetrics.functional import psnr, ssim
from data.illum_sys_dummy_dataset import y_illum_field_extract
from matplotlib import pyplot as plt
class Parallel_1ffSurf_Picture(pl.LightningModule):
"""
:param hps hyperparms, a parsed arg obj
"""
def __init__(self, hps, round_basin=True):
"""
"""
super(Parallel_1ffSurf_Picture, self).__init__()
# attrs
self.hps = copy.deepcopy(hps)
self.save_hyperparameters(self.hps)
self.lr = float(hps.lr)
# self.lr_decay_period = float(hps.lr_decay_period)
# self.lr_decay_factor = float(hps.lr_decay_factor)
# self.image_log_period = int(hps.image_log_period)
self.steepness = hps.steepness
self.illum_field_resolution = hps.resize_times * hps.target_img_res
self.field_weight_factor = hps.field_weight_factor
# sub models
self.ff_surf = RefraSurfs.CircleParallelSourcy(hps.aper_r,
hps.num_want_mizi,
allow_xy_move=hps.allow_xy_move)
self.target_plane = targetPlane.RectangleZ(hps.target_plane_z)
if round_basin:
self.basin = targetPlane.CircleBasinLoss(self.hps.aper_r)
else:
self.basin = targetPlane.RectangleBasinLoss(
[hps.basin_xmin, hps.basin_ymin],
[hps.basin_xmax, hps.basin_ymax])
self.register_buffer('field_sample_array', pointArrays.xy_range_array(self.illum_field_resolution,
self.illum_field_resolution,
hps.basin_xmin,
hps.basin_xmax,
hps.basin_ymin,
hps.basin_ymax))
self.register_buffer('y_illum_field', torch.tensor(y_illum_field_extract(hps)))
self.register_buffer('cached_illum_field', torch.zeros_like(self.y_illum_field))
def optimizer_step(self, epoch, batch_idx, optimizer, optimizer_idx, optimizer_closure=None, on_tpu=False,
using_native_amp=False, using_lbfgs=False):
optimizer.step()
def configure_optimizers(self):
if self.hps.optimizer == 'adam':
optimizer = torch.optim.Adam(self.parameters(), lr=self.lr)
elif self.hps.optimizer == 'sgd':
optimizer = torch.optim.SGD(self.parameters(), lr=self.lr)
else:
raise ValueError
# self.logger.experiment.add_text( # log_hyperparams() fails, searched, it is a bug, so use text instead
# 'hparam_str',
# str({
# "lr": self.lr,
# "lr_decay_period": self.lr_decay_period,
# # "steepness": self.hps.steepness,
# # "optimizer": self.hps.optimizer,
# # "field_weight_factor": self.hps.field_weight_factor, "resize_times": self.hps.resize_times
# }),
# 0)
#
lr_scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(
optimizer,
mode='min',
factor=0.9,
patience=10,
cooldown=5,
threshold=0.005,
threshold_mode='abs',
min_lr=1e-9,
verbose=True,
)
return {'optimizer': optimizer, 'lr_scheduler': lr_scheduler, 'monitor': 'validation/MSE'}
def trace_intersections(self):
p, k = self.ff_surf()
p_des = self.target_plane.intersect_ray_p_k(p, k)
p_des_xy = p_des[..., :-1]
return p_des_xy
def render_illumfield(self, p_des_xy):
prism_stack = targetPlane.triangular_prism_function(p_des_xy, self.field_sample_array, self.steepness)
illum_field = prism_stack.sum(0).sum(0)
illum_field = illum_field / illum_field.max() # standardization
return illum_field
def render_non_blur_illumfield(self, p_des_xy, steepness=100):
prism_stack = targetPlane.triangular_prism_function(p_des_xy, self.field_sample_array, steepness)
illum_field = prism_stack.sum(0).sum(0)
illum_field = illum_field / illum_field.max() # standardization
return illum_field
def basin_regulation(self, p_des_xy):
# this is for energy conservation and efficiency
z = self.basin(p_des_xy)
punishment = z.mean()
return punishment
def forward(self, *args, **kwargs):
p_des_xy = self.trace_intersections()
illum_field = self.render_illumfield(p_des_xy)
# imgTools.imshow(illum_field) # deb
return p_des_xy, illum_field
def training_step(self, batch, *args, **kwargs):
p_des_xy, illum_field = self.forward()
self.cached_illum_field = illum_field
basin_regu = self.basin_regulation(p_des_xy)
y_illum_field = self.y_illum_field # when using dummy or none dataloader
illum_field_mse = torch.nn.functional.mse_loss(illum_field, y_illum_field)
# smooth_regu = self.ff_surf.local_smooth_thresh_cos_regu()
smooth_regu = self.ff_surf.local_diff_relu_regu()
loss = basin_regu + smooth_regu + self.field_weight_factor * illum_field_mse
# an L2 loss and a#
self.log('train/basin_regu', basin_regu)
self.log('train/smooth_regu', smooth_regu)
self.log('train/illum_field_mse', illum_field_mse)
self.log('train_loss', loss) # keep this one, ckpt uses it!
# return {'p_des_xy': p_des_xy, 'y_illum_field': y_illum_field, 'illum_field': illum_field, 'loss': loss} # use with self.training_step_end()
return loss
def on_train_end(self): # log end status illumfield
p_des_xy, illum_field = self.forward()
self.logger.experiment.add_image(f'illum_field', illum_field.unsqueeze(0), self.global_step)
def validation_step(self, batch, *args, **kwargs):
# y_illum_field = batch.squeeze()
y_illum_field = self.y_illum_field # when using dummy or none dataloader
illum_field = self.cached_illum_field
self.logger.experiment.add_image(f'illum_field', illum_field.unsqueeze(0), self.global_step)
mat_save_path = os.path.join(self.logger.log_dir, '{:08d}_step.mat'.format(self.global_step))
util.mat_easy_save(illum_field, mat_save_path)
# ↑ mind that ensure upper level folders exit before this
mse_val = torch.nn.functional.mse_loss(illum_field, y_illum_field)
self.log('validation/MSE', mse_val)
psnr_val = psnr(illum_field.unsqueeze(0).unsqueeze(0),
y_illum_field.unsqueeze(0).unsqueeze(0))
ssim_val = ssim(illum_field.unsqueeze(0).unsqueeze(0),
y_illum_field.unsqueeze(0).unsqueeze(0))
self.log('validation/PSNR', psnr_val)
self.log('validation/SSIM', ssim_val)
self.log('lr', self.optimizers().state_dict()['param_groups'][0]['lr'])
# return {'mse':mse_val,'ssim':ssim_val, 'psnr':psnr_val }
# def validation_epoch_end(self, outputs): # optional
# oout_put = outputs[0]
# pass
if __name__ == '__main__':
pass
#
# # num_want_mizi = 32 ** 2 # deb
# num_want_mizi = 32 # deb
# z_target_plane = 300
# sample_half_width = 20.
# illum_field_resolution = 128
# steepness = 100
# a_surf = RefraSurfs.CircleParallelSourcy(25.4, num_want_mizi=num_want_mizi)
# a_plane = targetPlane.RectangleZ(z_target_plane)
# p, k = a_surf()
# p_des = a_plane.intersect_ray_p_k(p, k)
# p_des_xy = p_des[..., :-1]
#
# prism_stack = targetPlane.triangular_prism_function(p_des_xy, field_sample_array, steepness)
# # using prism func to make field
# imgTools.imshow(prism_stack[0, 0, ...])
#
# imgTools.imshow(prism_stack.sum(0).sum(0))
#
# visTools.scatter_xyz(p_des) # show intersections
# print(a_surf.facet_normals())
# a_surf.plot_vertex_3d()
# n = a_surf.facet_normals()
# print(n.shape)
# print(p.shape)
# print(k.shape)