import argparse import inspect from backbone import EncoderUNetModel, UNetModel from .gaussian_diffusion import (LossType, ModelMeanType, ModelVarType, get_named_beta_schedule) from .respace import SpacedDiffusion, space_timesteps def create_model( image_size, num_channels, num_res_blocks, channel_mult="", learn_sigma=False, class_cond=False, use_checkpoint=False, attention_resolutions="16", num_heads=1, num_head_channels=-1, num_heads_upsample=-1, use_scale_shift_norm=False, dropout=0, resblock_updown=False, use_fp16=False, use_new_attention_order=False, num_classes_1=None, num_classes_2=None, isic=False, ): if channel_mult == "": if image_size == 512: channel_mult = (0.5, 1, 1, 2, 2, 4, 4) elif image_size == 256: channel_mult = (1, 1, 2, 2, 4, 4) elif image_size == 128: channel_mult = (1, 1, 2, 3, 4) elif image_size == 64: channel_mult = (1, 2, 3, 4) else: raise ValueError(f"unsupported image size: {image_size}") else: channel_mult = tuple(int(ch_mult) for ch_mult in channel_mult.split(",")) attention_ds = [] for res in attention_resolutions.split(","): attention_ds.append(image_size // int(res)) return UNetModel( image_size=image_size, in_channels=(1 if not isic else 3), model_channels=num_channels, out_channels=((1 if not learn_sigma else 2) if not isic else (3 if not learn_sigma else 6)), num_res_blocks=num_res_blocks, attention_resolutions=tuple(attention_ds), dropout=dropout, channel_mult=channel_mult, num_classes_1=(num_classes_1 if class_cond else None), num_classes_2=(num_classes_2 if class_cond else None), use_checkpoint=use_checkpoint, use_fp16=use_fp16, num_heads=num_heads, num_head_channels=num_head_channels, num_heads_upsample=num_heads_upsample, use_scale_shift_norm=use_scale_shift_norm, resblock_updown=resblock_updown, use_new_attention_order=use_new_attention_order, ) def create_gaussian_diffusion( *, steps=1000, learn_sigma=False, sigma_small=False, noise_schedule="linear", use_kl=False, predict_xstart=False, rescale_timesteps=False, rescale_learned_sigmas=False, timestep_respacing="", ): betas = get_named_beta_schedule(noise_schedule, steps) if use_kl: loss_type = LossType.RESCALED_KL elif rescale_learned_sigmas: loss_type = LossType.RESCALED_MSE else: loss_type = LossType.MSE if not timestep_respacing: timestep_respacing = [steps] return SpacedDiffusion( use_timesteps=space_timesteps(steps, timestep_respacing), betas=betas, model_mean_type=( ModelMeanType.EPSILON if not predict_xstart else ModelMeanType.START_X ), model_var_type=( ( ModelVarType.FIXED_LARGE if not sigma_small else ModelVarType.FIXED_SMALL ) if not learn_sigma else ModelVarType.LEARNED_RANGE ), loss_type=loss_type, rescale_timesteps=rescale_timesteps, ) def create_classifier(isic=False): image_size = 256 in_channels = 1 if not isic else 3 model_channels = 64 out_channels = 1 num_res_blocks = 1 channel_mult = (1, 2, 2, 4) attention_resolutions = [16] return EncoderUNetModel( image_size, in_channels, model_channels, out_channels, num_res_blocks, attention_resolutions, dropout=0, channel_mult=channel_mult, conv_resample=True, dims=2, num_classes_1=None, num_classes_2=None, use_checkpoint=False, use_fp16=False, num_heads=1, num_head_channels=4, num_heads_upsample=-1, use_scale_shift_norm=True, resblock_updown=True, use_new_attention_order=False, ) def create_model_and_diffusion( image_size, class_cond, learn_sigma, num_channels, num_res_blocks, channel_mult, num_heads, num_head_channels, num_heads_upsample, attention_resolutions, dropout, diffusion_steps, noise_schedule, timestep_respacing, use_kl, predict_xstart, rescale_timesteps, rescale_learned_sigmas, use_checkpoint, use_scale_shift_norm, resblock_updown, use_fp16, use_new_attention_order, num_classes_1, num_classes_2, isic, ): model = create_model( image_size, num_channels, num_res_blocks, channel_mult=channel_mult, learn_sigma=learn_sigma, class_cond=class_cond, use_checkpoint=use_checkpoint, attention_resolutions=attention_resolutions, num_heads=num_heads, num_head_channels=num_head_channels, num_heads_upsample=num_heads_upsample, use_scale_shift_norm=use_scale_shift_norm, dropout=dropout, resblock_updown=resblock_updown, use_fp16=use_fp16, use_new_attention_order=use_new_attention_order, num_classes_1=num_classes_1, num_classes_2=num_classes_2, isic=isic, ) diffusion = create_gaussian_diffusion( steps=diffusion_steps, learn_sigma=learn_sigma, noise_schedule=noise_schedule, use_kl=use_kl, predict_xstart=predict_xstart, rescale_timesteps=rescale_timesteps, rescale_learned_sigmas=rescale_learned_sigmas, timestep_respacing=timestep_respacing, ) return model, diffusion def create_classifier_and_diffusion( image_size, classifier_use_fp16, classifier_width, classifier_depth, classifier_attention_resolutions, classifier_use_scale_shift_norm, classifier_resblock_updown, classifier_pool, learn_sigma, diffusion_steps, noise_schedule, timestep_respacing, use_kl, predict_xstart, rescale_timesteps, rescale_learned_sigmas, num_classes_1, num_classes_2, isic ): classifier = create_classifier(isic) diffusion = create_gaussian_diffusion( steps=diffusion_steps, learn_sigma=learn_sigma, noise_schedule=noise_schedule, use_kl=use_kl, predict_xstart=predict_xstart, rescale_timesteps=rescale_timesteps, rescale_learned_sigmas=rescale_learned_sigmas, timestep_respacing=timestep_respacing, ) return classifier, diffusion