106 lines
4.3 KiB
Python
106 lines
4.3 KiB
Python
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# Copyright (c) 2022, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
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#
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# NVIDIA CORPORATION & AFFILIATES and its licensors retain all intellectual property
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# and proprietary rights in and to this software, related documentation
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# and any modifications thereto. Any use, reproduction, disclosure or
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# distribution of this software and related documentation without an express
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# license agreement from NVIDIA CORPORATION & AFFILIATES is strictly prohibited.
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import torch
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import torch.nn.functional as F
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import torch.nn as nn
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import numpy as np
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from loguru import logger
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from utils.vis_helper import visualize_point_clouds_3d
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from utils.data_helper import normalize_point_clouds
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from utils.checker import *
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@torch.no_grad()
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def validate_inspect_noprior(model,
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it, writer,
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sample_num_points, num_samples,
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need_sample=1, need_val=1, need_train=1,
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w_prior=None, val_x=None, tr_x=None,
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test_loader=None, # can be None
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has_shapelatent=False,
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bound=1.5, val_class_label=None, tr_class_label=None,
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cfg={}):
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""" visualize the samples, and recont if needed
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"""
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assert(has_shapelatent)
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assert(w_prior is not None and val_x is not None and tr_x is not None)
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z_list = []
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num_samples = w_prior.shape[0] if need_sample else 0
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num_recon = val_x.shape[0]
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num_recon_val = num_recon if need_val else 0
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num_recon_train = num_recon if need_train else 0
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assert(need_sample == 0 and need_val > 0 and need_train == 0)
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if need_sample:
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z_prior = model.pz(w_prior, sample_num_points)
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z_list.append(z_prior)
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if val_class_label is not None:
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output = model.recont(val_x, class_label=val_class_label)
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else:
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output = model.recont(val_x) # torch.cat([val_x, tr_x]))
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gen_x = output['final_pred']
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vis_order = cfg.viz.viz_order
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vis_args = {'vis_order': vis_order}
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# vis the samples
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if num_samples > 0:
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img_list = []
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for i in range(num_samples):
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points = gen_x[i]
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points = normalize_point_clouds([points])[0]
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img = visualize_point_clouds_3d([points], bound=bound, **vis_args)
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img_list.append(img)
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img = np.concatenate(img_list, axis=2)
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writer.add_image('sample', torch.as_tensor(img), it)
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# vis the recont
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if num_recon_val > 0:
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img_list = []
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for i in range(num_recon_val):
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points = gen_x[num_samples + i]
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points = normalize_point_clouds([points]) # val_x[i], points])
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img = visualize_point_clouds_3d(
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points, ['rec#%d' % i], bound=bound, **vis_args)
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img_list.append(img)
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gt_list = []
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for i in range(num_recon_val):
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points = normalize_point_clouds([val_x[i]])
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img = visualize_point_clouds_3d(
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points, ['gt#%d' % i], bound=bound, **vis_args)
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gt_list.append(img)
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img = np.concatenate(img_list, axis=2)
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gt = np.concatenate(gt_list, axis=2)
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img = np.concatenate([gt, img], axis=1)
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if 'vis/latent_pts' in output:
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latent_pts = output['vis/latent_pts']
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img_list = []
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for i in range(num_recon_val):
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points = latent_pts[num_samples + i]
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points = normalize_point_clouds([points])
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latent = visualize_point_clouds_3d(
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points, ['latent#%d' % i], bound=bound, **vis_args)
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img_list.append(latent)
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latent_list = np.concatenate(img_list, axis=2)
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img = np.concatenate([img, latent_list], axis=1)
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writer.add_image('valrecont', torch.as_tensor(img), it)
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if num_recon_train > 0:
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img_list = []
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for i in range(num_recon_train):
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points = gen_x[num_samples + num_recon_val + i]
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points = normalize_point_clouds([tr_x[i], points])
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img = visualize_point_clouds_3d(
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points, ['ori', 'rec'], bound=bound, **vis_args)
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img_list.append(img)
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img = np.concatenate(img_list, axis=2)
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writer.add_image('train/recont', torch.as_tensor(img), it)
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logger.info('writer: {}', writer.url)
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