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import os |
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import os.path as osp |
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import cv2 |
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import torch |
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import torch.nn.functional as F |
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import numpy as np |
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from infinity.schedules.dynamic_resolution import get_first_full_spatial_size_scale_index |
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def labels2image(all_indices, label_type='int_label', scale_schedule=None): |
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summed_codes, recons_imgs = self.vae.decode_from_indices(all_indices, scale_schedule, label_type) |
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recons_img = recons_imgs[0] |
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recons_img = (recons_img + 1) / 2 |
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recons_img = recons_img.permute(1, 2, 0).mul_(255).cpu().numpy().astype(np.uint8)[:,:,::-1] |
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return recons_img |
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def features2image(raw_features): |
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recons_imgs = self.vae.decode(raw_features.squeeze(-3)) |
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recons_img = recons_imgs[0] |
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recons_img = (recons_img + 1) / 2 |
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recons_img = recons_img.permute(1, 2, 0).mul_(255).cpu().numpy().astype(np.uint8)[:,:,::-1] |
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return recons_img |
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class SelfCorrection(object): |
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def __init__(self, vae, args): |
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self.noise_apply_layers = args.noise_apply_layers |
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self.noise_apply_requant = args.noise_apply_requant |
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self.noise_apply_strength = args.noise_apply_strength |
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if not isinstance(self.noise_apply_strength, list): |
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self.noise_apply_strength = str(self.noise_apply_strength) |
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self.noise_apply_strength = list(map(float, self.noise_apply_strength.split(','))) |
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if len(self.noise_apply_strength) == 1: |
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self.noise_apply_strength = self.noise_apply_strength[0] |
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self.apply_spatial_patchify = args.apply_spatial_patchify |
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self.vae = vae |
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print(f'self.noise_apply_strength: {self.noise_apply_strength}') |
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def apply_noise_requant(self, bit_indices, quantized, args, device, si, lfq=None, noise_apply_strength=None): |
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if lfq is None: |
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lfq = self.vae.quantizer.lfq |
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if noise_apply_strength is None: |
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noise_apply_strength = self.noise_apply_strength |
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if isinstance(noise_apply_strength, list): |
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noise_apply_strength = np.random.randint(0, max(1, 100 * noise_apply_strength[si]+1)) * 0.01 |
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else: |
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noise_apply_strength = np.random.randint(0, max(1, 100 * noise_apply_strength+1)) * 0.01 |
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mask = torch.rand(*bit_indices.shape, device=device) < noise_apply_strength |
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pred_bit_indices = bit_indices.clone() |
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if args.num_of_label_value == 2: |
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pred_bit_indices[mask] = 1 - pred_bit_indices[mask] |
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else: |
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noise = torch.randint(0, args.num_of_label_value, bit_indices.shape, dtype=bit_indices.dtype, device=device) |
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pred_bit_indices[mask] = noise[mask] |
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if self.noise_apply_requant: |
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quantized = lfq.indices_to_codes(pred_bit_indices, label_type = 'bit_label') |
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return pred_bit_indices, quantized |
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def visualize(self, vae_scale_schedule, inp_B3HW, gt_all_bit_indices, pred_all_bit_indices): |
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gt_img = (inp_B3HW.squeeze(-3) + 1) / 2 * 255 |
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gt_img = gt_img[0].permute(1,2,0).cpu().numpy().astype(np.uint8)[:,:,::-1] |
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recons_img_2 = labels2image(gt_all_bit_indices, label_type='bit_label', scale_schedule=vae_scale_schedule) |
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recons_img_3 = labels2image(pred_all_bit_indices, label_type='bit_label', scale_schedule=vae_scale_schedule) |
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cat_image = np.concatenate([gt_img, recons_img_2, recons_img_3], axis=1) |
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save_path = osp.abspath('non_teacher_force.jpg') |
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cv2.imwrite(save_path, cat_image) |
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print(f'Save to {save_path}') |
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import pdb; pdb.set_trace() |
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print(cat_image.shape) |
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