Delete extensions-builtin/forge_preprocessor_inpaint

extensions-builtin/forge_preprocessor_inpaint/annotator/lama/saicinpainting/training/data/masks.py

@@ -1,332 +0,0 @@
-import math
-import random
-import hashlib
-import logging
-from enum import Enum
-
-import cv2
-import numpy as np
-
-# from annotator.lama.saicinpainting.evaluation.masks.mask import SegmentationMask
-from annotator.lama.saicinpainting.utils import LinearRamp
-
-LOGGER = logging.getLogger(__name__)
-
-
-class DrawMethod(Enum):
-    LINE = 'line'
-    CIRCLE = 'circle'
-    SQUARE = 'square'
-
-
-def make_random_irregular_mask(shape, max_angle=4, max_len=60, max_width=20, min_times=0, max_times=10,
-                               draw_method=DrawMethod.LINE):
-    draw_method = DrawMethod(draw_method)
-
-    height, width = shape
-    mask = np.zeros((height, width), np.float32)
-    times = np.random.randint(min_times, max_times + 1)
-    for i in range(times):
-        start_x = np.random.randint(width)
-        start_y = np.random.randint(height)
-        for j in range(1 + np.random.randint(5)):
-            angle = 0.01 + np.random.randint(max_angle)
-            if i % 2 == 0:
-                angle = 2 * 3.1415926 - angle
-            length = 10 + np.random.randint(max_len)
-            brush_w = 5 + np.random.randint(max_width)
-            end_x = np.clip((start_x + length * np.sin(angle)).astype(np.int32), 0, width)
-            end_y = np.clip((start_y + length * np.cos(angle)).astype(np.int32), 0, height)
-            if draw_method == DrawMethod.LINE:
-                cv2.line(mask, (start_x, start_y), (end_x, end_y), 1.0, brush_w)
-            elif draw_method == DrawMethod.CIRCLE:
-                cv2.circle(mask, (start_x, start_y), radius=brush_w, color=1., thickness=-1)
-            elif draw_method == DrawMethod.SQUARE:
-                radius = brush_w // 2
-                mask[start_y - radius:start_y + radius, start_x - radius:start_x + radius] = 1
-            start_x, start_y = end_x, end_y
-    return mask[None, ...]
-
-
-class RandomIrregularMaskGenerator:
-    def __init__(self, max_angle=4, max_len=60, max_width=20, min_times=0, max_times=10, ramp_kwargs=None,
-                 draw_method=DrawMethod.LINE):
-        self.max_angle = max_angle
-        self.max_len = max_len
-        self.max_width = max_width
-        self.min_times = min_times
-        self.max_times = max_times
-        self.draw_method = draw_method
-        self.ramp = LinearRamp(**ramp_kwargs) if ramp_kwargs is not None else None
-
-    def __call__(self, img, iter_i=None, raw_image=None):
-        coef = self.ramp(iter_i) if (self.ramp is not None) and (iter_i is not None) else 1
-        cur_max_len = int(max(1, self.max_len * coef))
-        cur_max_width = int(max(1, self.max_width * coef))
-        cur_max_times = int(self.min_times + 1 + (self.max_times - self.min_times) * coef)
-        return make_random_irregular_mask(img.shape[1:], max_angle=self.max_angle, max_len=cur_max_len,
-                                          max_width=cur_max_width, min_times=self.min_times, max_times=cur_max_times,
-                                          draw_method=self.draw_method)
-
-
-def make_random_rectangle_mask(shape, margin=10, bbox_min_size=30, bbox_max_size=100, min_times=0, max_times=3):
-    height, width = shape
-    mask = np.zeros((height, width), np.float32)
-    bbox_max_size = min(bbox_max_size, height - margin * 2, width - margin * 2)
-    times = np.random.randint(min_times, max_times + 1)
-    for i in range(times):
-        box_width = np.random.randint(bbox_min_size, bbox_max_size)
-        box_height = np.random.randint(bbox_min_size, bbox_max_size)
-        start_x = np.random.randint(margin, width - margin - box_width + 1)
-        start_y = np.random.randint(margin, height - margin - box_height + 1)
-        mask[start_y:start_y + box_height, start_x:start_x + box_width] = 1
-    return mask[None, ...]
-
-
-class RandomRectangleMaskGenerator:
-    def __init__(self, margin=10, bbox_min_size=30, bbox_max_size=100, min_times=0, max_times=3, ramp_kwargs=None):
-        self.margin = margin
-        self.bbox_min_size = bbox_min_size
-        self.bbox_max_size = bbox_max_size
-        self.min_times = min_times
-        self.max_times = max_times
-        self.ramp = LinearRamp(**ramp_kwargs) if ramp_kwargs is not None else None
-
-    def __call__(self, img, iter_i=None, raw_image=None):
-        coef = self.ramp(iter_i) if (self.ramp is not None) and (iter_i is not None) else 1
-        cur_bbox_max_size = int(self.bbox_min_size + 1 + (self.bbox_max_size - self.bbox_min_size) * coef)
-        cur_max_times = int(self.min_times + (self.max_times - self.min_times) * coef)
-        return make_random_rectangle_mask(img.shape[1:], margin=self.margin, bbox_min_size=self.bbox_min_size,
-                                          bbox_max_size=cur_bbox_max_size, min_times=self.min_times,
-                                          max_times=cur_max_times)
-
-
-class RandomSegmentationMaskGenerator:
-    def __init__(self, **kwargs):
-        self.impl = None  # will be instantiated in first call (effectively in subprocess)
-        self.kwargs = kwargs
-
-    def __call__(self, img, iter_i=None, raw_image=None):
-        if self.impl is None:
-            self.impl = SegmentationMask(**self.kwargs)
-
-        masks = self.impl.get_masks(np.transpose(img, (1, 2, 0)))
-        masks = [m for m in masks if len(np.unique(m)) > 1]
-        return np.random.choice(masks)
-
-
-def make_random_superres_mask(shape, min_step=2, max_step=4, min_width=1, max_width=3):
-    height, width = shape
-    mask = np.zeros((height, width), np.float32)
-    step_x = np.random.randint(min_step, max_step + 1)
-    width_x = np.random.randint(min_width, min(step_x, max_width + 1))
-    offset_x = np.random.randint(0, step_x)
-
-    step_y = np.random.randint(min_step, max_step + 1)
-    width_y = np.random.randint(min_width, min(step_y, max_width + 1))
-    offset_y = np.random.randint(0, step_y)
-
-    for dy in range(width_y):
-        mask[offset_y + dy::step_y] = 1
-    for dx in range(width_x):
-        mask[:, offset_x + dx::step_x] = 1
-    return mask[None, ...]
-
-
-class RandomSuperresMaskGenerator:
-    def __init__(self, **kwargs):
-        self.kwargs = kwargs
-
-    def __call__(self, img, iter_i=None):
-        return make_random_superres_mask(img.shape[1:], **self.kwargs)
-
-
-class DumbAreaMaskGenerator:
-    min_ratio = 0.1
-    max_ratio = 0.35
-    default_ratio = 0.225
-
-    def __init__(self, is_training):
-        #Parameters:
-        #    is_training(bool): If true - random rectangular mask, if false - central square mask
-        self.is_training = is_training
-
-    def _random_vector(self, dimension):
-        if self.is_training:
-            lower_limit = math.sqrt(self.min_ratio)
-            upper_limit = math.sqrt(self.max_ratio)
-            mask_side = round((random.random() * (upper_limit - lower_limit) + lower_limit) * dimension)
-            u = random.randint(0, dimension-mask_side-1)
-            v = u+mask_side 
-        else:
-            margin = (math.sqrt(self.default_ratio) / 2) * dimension
-            u = round(dimension/2 - margin)
-            v = round(dimension/2 + margin)
-        return u, v
-
-    def __call__(self, img, iter_i=None, raw_image=None):
-        c, height, width = img.shape
-        mask = np.zeros((height, width), np.float32)
-        x1, x2 = self._random_vector(width)
-        y1, y2 = self._random_vector(height)
-        mask[x1:x2, y1:y2] = 1
-        return mask[None, ...]
-
-
-class OutpaintingMaskGenerator:
-    def __init__(self, min_padding_percent:float=0.04, max_padding_percent:int=0.25, left_padding_prob:float=0.5, top_padding_prob:float=0.5, 
-                 right_padding_prob:float=0.5, bottom_padding_prob:float=0.5, is_fixed_randomness:bool=False):
-        """
-        is_fixed_randomness - get identical paddings for the same image if args are the same
-        """
-        self.min_padding_percent = min_padding_percent
-        self.max_padding_percent = max_padding_percent
-        self.probs = [left_padding_prob, top_padding_prob, right_padding_prob, bottom_padding_prob]
-        self.is_fixed_randomness = is_fixed_randomness
-
-        assert self.min_padding_percent <= self.max_padding_percent
-        assert self.max_padding_percent > 0
-        assert len([x for x in [self.min_padding_percent, self.max_padding_percent] if (x>=0 and x<=1)]) == 2, f"Padding percentage should be in [0,1]"
-        assert sum(self.probs) > 0, f"At least one of the padding probs should be greater than 0 - {self.probs}"
-        assert len([x for x in self.probs if (x >= 0) and (x <= 1)]) == 4, f"At least one of padding probs is not in [0,1] - {self.probs}"
-        if len([x for x in self.probs if x > 0]) == 1:
-            LOGGER.warning(f"Only one padding prob is greater than zero - {self.probs}. That means that the outpainting masks will be always on the same side")
-
-    def apply_padding(self, mask, coord):
-        mask[int(coord[0][0]*self.img_h):int(coord[1][0]*self.img_h),   
-             int(coord[0][1]*self.img_w):int(coord[1][1]*self.img_w)] = 1
-        return mask
-
-    def get_padding(self, size):
-        n1 = int(self.min_padding_percent*size)
-        n2 = int(self.max_padding_percent*size)
-        return self.rnd.randint(n1, n2) / size
-
-    @staticmethod
-    def _img2rs(img):
-        arr = np.ascontiguousarray(img.astype(np.uint8))
-        str_hash = hashlib.sha1(arr).hexdigest()
-        res = hash(str_hash)%(2**32)
-        return res
-
-    def __call__(self, img, iter_i=None, raw_image=None):
-        c, self.img_h, self.img_w = img.shape
-        mask = np.zeros((self.img_h, self.img_w), np.float32)
-        at_least_one_mask_applied = False
-
-        if self.is_fixed_randomness:
-            assert raw_image is not None, f"Cant calculate hash on raw_image=None"
-            rs = self._img2rs(raw_image)
-            self.rnd = np.random.RandomState(rs)
-        else:
-            self.rnd = np.random
-
-        coords = [[
-                   (0,0), 
-                   (1,self.get_padding(size=self.img_h))
-                  ],
-                  [
-                    (0,0),
-                    (self.get_padding(size=self.img_w),1)
-                  ],
-                  [
-                    (0,1-self.get_padding(size=self.img_h)),
-                    (1,1)
-                  ],    
-                  [
-                    (1-self.get_padding(size=self.img_w),0),
-                    (1,1)
-                  ]]
-
-        for pp, coord in zip(self.probs, coords):
-            if self.rnd.random() < pp:
-                at_least_one_mask_applied = True
-                mask = self.apply_padding(mask=mask, coord=coord)
-
-        if not at_least_one_mask_applied:
-            idx = self.rnd.choice(range(len(coords)), p=np.array(self.probs)/sum(self.probs))
-            mask = self.apply_padding(mask=mask, coord=coords[idx])
-        return mask[None, ...]
-
-
-class MixedMaskGenerator:
-    def __init__(self, irregular_proba=1/3, irregular_kwargs=None,
-                 box_proba=1/3, box_kwargs=None,
-                 segm_proba=1/3, segm_kwargs=None,
-                 squares_proba=0, squares_kwargs=None,
-                 superres_proba=0, superres_kwargs=None,
-                 outpainting_proba=0, outpainting_kwargs=None,
-                 invert_proba=0):
-        self.probas = []
-        self.gens = []
-
-        if irregular_proba > 0:
-            self.probas.append(irregular_proba)
-            if irregular_kwargs is None:
-                irregular_kwargs = {}
-            else:
-                irregular_kwargs = dict(irregular_kwargs)
-            irregular_kwargs['draw_method'] = DrawMethod.LINE
-            self.gens.append(RandomIrregularMaskGenerator(**irregular_kwargs))
-
-        if box_proba > 0:
-            self.probas.append(box_proba)
-            if box_kwargs is None:
-                box_kwargs = {}
-            self.gens.append(RandomRectangleMaskGenerator(**box_kwargs))
-
-        if segm_proba > 0:
-            self.probas.append(segm_proba)
-            if segm_kwargs is None:
-                segm_kwargs = {}
-            self.gens.append(RandomSegmentationMaskGenerator(**segm_kwargs))
-
-        if squares_proba > 0:
-            self.probas.append(squares_proba)
-            if squares_kwargs is None:
-                squares_kwargs = {}
-            else:
-                squares_kwargs = dict(squares_kwargs)
-            squares_kwargs['draw_method'] = DrawMethod.SQUARE
-            self.gens.append(RandomIrregularMaskGenerator(**squares_kwargs))
-
-        if superres_proba > 0:
-            self.probas.append(superres_proba)
-            if superres_kwargs is None:
-                superres_kwargs = {}
-            self.gens.append(RandomSuperresMaskGenerator(**superres_kwargs))
-
-        if outpainting_proba > 0:
-            self.probas.append(outpainting_proba)
-            if outpainting_kwargs is None:
-                outpainting_kwargs = {}
-            self.gens.append(OutpaintingMaskGenerator(**outpainting_kwargs))
-
-        self.probas = np.array(self.probas, dtype='float32')
-        self.probas /= self.probas.sum()
-        self.invert_proba = invert_proba
-
-    def __call__(self, img, iter_i=None, raw_image=None):
-        kind = np.random.choice(len(self.probas), p=self.probas)
-        gen = self.gens[kind]
-        result = gen(img, iter_i=iter_i, raw_image=raw_image)
-        if self.invert_proba > 0 and random.random() < self.invert_proba:
-            result = 1 - result
-        return result
-
-
-def get_mask_generator(kind, kwargs):
-    if kind is None:
-        kind = "mixed"
-    if kwargs is None:
-        kwargs = {}
-
-    if kind == "mixed":
-        cl = MixedMaskGenerator
-    elif kind == "outpainting":
-        cl = OutpaintingMaskGenerator
-    elif kind == "dumb":
-        cl = DumbAreaMaskGenerator
-    else:
-        raise NotImplementedError(f"No such generator kind = {kind}")
-    return cl(**kwargs)

extensions-builtin/forge_preprocessor_inpaint/annotator/lama/saicinpainting/training/losses/adversarial.py

@@ -1,177 +0,0 @@
-from typing import Tuple, Dict, Optional
-
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-
-
-class BaseAdversarialLoss:
-    def pre_generator_step(self, real_batch: torch.Tensor, fake_batch: torch.Tensor,
-                           generator: nn.Module, discriminator: nn.Module):
-        """
-        Prepare for generator step
-        :param real_batch: Tensor, a batch of real samples
-        :param fake_batch: Tensor, a batch of samples produced by generator
-        :param generator:
-        :param discriminator:
-        :return: None
-        """
-
-    def pre_discriminator_step(self, real_batch: torch.Tensor, fake_batch: torch.Tensor,
-                               generator: nn.Module, discriminator: nn.Module):
-        """
-        Prepare for discriminator step
-        :param real_batch: Tensor, a batch of real samples
-        :param fake_batch: Tensor, a batch of samples produced by generator
-        :param generator:
-        :param discriminator:
-        :return: None
-        """
-
-    def generator_loss(self, real_batch: torch.Tensor, fake_batch: torch.Tensor,
-                       discr_real_pred: torch.Tensor, discr_fake_pred: torch.Tensor,
-                       mask: Optional[torch.Tensor] = None) \
-            -> Tuple[torch.Tensor, Dict[str, torch.Tensor]]:
-        """
-        Calculate generator loss
-        :param real_batch: Tensor, a batch of real samples
-        :param fake_batch: Tensor, a batch of samples produced by generator
-        :param discr_real_pred: Tensor, discriminator output for real_batch
-        :param discr_fake_pred: Tensor, discriminator output for fake_batch
-        :param mask: Tensor, actual mask, which was at input of generator when making fake_batch
-        :return: total generator loss along with some values that might be interesting to log
-        """
-        raise NotImplemented()
-
-    def discriminator_loss(self, real_batch: torch.Tensor, fake_batch: torch.Tensor,
-                           discr_real_pred: torch.Tensor, discr_fake_pred: torch.Tensor,
-                           mask: Optional[torch.Tensor] = None) \
-            -> Tuple[torch.Tensor, Dict[str, torch.Tensor]]:
-        """
-        Calculate discriminator loss and call .backward() on it
-        :param real_batch: Tensor, a batch of real samples
-        :param fake_batch: Tensor, a batch of samples produced by generator
-        :param discr_real_pred: Tensor, discriminator output for real_batch
-        :param discr_fake_pred: Tensor, discriminator output for fake_batch
-        :param mask: Tensor, actual mask, which was at input of generator when making fake_batch
-        :return: total discriminator loss along with some values that might be interesting to log
-        """
-        raise NotImplemented()
-
-    def interpolate_mask(self, mask, shape):
-        assert mask is not None
-        assert self.allow_scale_mask or shape == mask.shape[-2:]
-        if shape != mask.shape[-2:] and self.allow_scale_mask:
-            if self.mask_scale_mode == 'maxpool':
-                mask = F.adaptive_max_pool2d(mask, shape)
-            else:
-                mask = F.interpolate(mask, size=shape, mode=self.mask_scale_mode)
-        return mask
-
-def make_r1_gp(discr_real_pred, real_batch):
-    if torch.is_grad_enabled():
-        grad_real = torch.autograd.grad(outputs=discr_real_pred.sum(), inputs=real_batch, create_graph=True)[0]
-        grad_penalty = (grad_real.view(grad_real.shape[0], -1).norm(2, dim=1) ** 2).mean()
-    else:
-        grad_penalty = 0
-    real_batch.requires_grad = False
-
-    return grad_penalty
-
-class NonSaturatingWithR1(BaseAdversarialLoss):
-    def __init__(self, gp_coef=5, weight=1, mask_as_fake_target=False, allow_scale_mask=False,
-                 mask_scale_mode='nearest', extra_mask_weight_for_gen=0,
-                 use_unmasked_for_gen=True, use_unmasked_for_discr=True):
-        self.gp_coef = gp_coef
-        self.weight = weight
-        # use for discr => use for gen;
-        # otherwise we teach only the discr to pay attention to very small difference
-        assert use_unmasked_for_gen or (not use_unmasked_for_discr)
-        # mask as target => use unmasked for discr:
-        # if we don't care about unmasked regions at all
-        # then it doesn't matter if the value of mask_as_fake_target is true or false
-        assert use_unmasked_for_discr or (not mask_as_fake_target)
-        self.use_unmasked_for_gen = use_unmasked_for_gen
-        self.use_unmasked_for_discr = use_unmasked_for_discr
-        self.mask_as_fake_target = mask_as_fake_target
-        self.allow_scale_mask = allow_scale_mask
-        self.mask_scale_mode = mask_scale_mode
-        self.extra_mask_weight_for_gen = extra_mask_weight_for_gen
-
-    def generator_loss(self, real_batch: torch.Tensor, fake_batch: torch.Tensor,
-                       discr_real_pred: torch.Tensor, discr_fake_pred: torch.Tensor,
-                       mask=None) \
-            -> Tuple[torch.Tensor, Dict[str, torch.Tensor]]:
-        fake_loss = F.softplus(-discr_fake_pred)
-        if (self.mask_as_fake_target and self.extra_mask_weight_for_gen > 0) or \
-                not self.use_unmasked_for_gen:  # == if masked region should be treated differently
-            mask = self.interpolate_mask(mask, discr_fake_pred.shape[-2:])
-            if not self.use_unmasked_for_gen:
-                fake_loss = fake_loss * mask
-            else:
-                pixel_weights = 1 + mask * self.extra_mask_weight_for_gen
-                fake_loss = fake_loss * pixel_weights
-
-        return fake_loss.mean() * self.weight, dict()
-
-    def pre_discriminator_step(self, real_batch: torch.Tensor, fake_batch: torch.Tensor,
-                               generator: nn.Module, discriminator: nn.Module):
-        real_batch.requires_grad = True
-
-    def discriminator_loss(self, real_batch: torch.Tensor, fake_batch: torch.Tensor,
-                           discr_real_pred: torch.Tensor, discr_fake_pred: torch.Tensor,
-                           mask=None) \
-            -> Tuple[torch.Tensor, Dict[str, torch.Tensor]]:
-
-        real_loss = F.softplus(-discr_real_pred)
-        grad_penalty = make_r1_gp(discr_real_pred, real_batch) * self.gp_coef
-        fake_loss = F.softplus(discr_fake_pred)
-
-        if not self.use_unmasked_for_discr or self.mask_as_fake_target:
-            # == if masked region should be treated differently
-            mask = self.interpolate_mask(mask, discr_fake_pred.shape[-2:])
-            # use_unmasked_for_discr=False only makes sense for fakes;
-            # for reals there is no difference beetween two regions
-            fake_loss = fake_loss * mask
-            if self.mask_as_fake_target:
-                fake_loss = fake_loss + (1 - mask) * F.softplus(-discr_fake_pred)
-
-        sum_discr_loss = real_loss + grad_penalty + fake_loss
-        metrics = dict(discr_real_out=discr_real_pred.mean(),
-                       discr_fake_out=discr_fake_pred.mean(),
-                       discr_real_gp=grad_penalty)
-        return sum_discr_loss.mean(), metrics
-
-class BCELoss(BaseAdversarialLoss):
-    def __init__(self, weight):
-        self.weight = weight
-        self.bce_loss = nn.BCEWithLogitsLoss()
-
-    def generator_loss(self, discr_fake_pred: torch.Tensor) -> Tuple[torch.Tensor, Dict[str, torch.Tensor]]:
-        real_mask_gt = torch.zeros(discr_fake_pred.shape).to(discr_fake_pred.device)
-        fake_loss = self.bce_loss(discr_fake_pred, real_mask_gt) * self.weight
-        return fake_loss, dict()
-
-    def pre_discriminator_step(self, real_batch: torch.Tensor, fake_batch: torch.Tensor,
-                               generator: nn.Module, discriminator: nn.Module):
-        real_batch.requires_grad = True
-
-    def discriminator_loss(self,
-                           mask: torch.Tensor,
-                           discr_real_pred: torch.Tensor,
-                           discr_fake_pred: torch.Tensor) -> Tuple[torch.Tensor, Dict[str, torch.Tensor]]:
-
-        real_mask_gt = torch.zeros(discr_real_pred.shape).to(discr_real_pred.device)
-        sum_discr_loss = (self.bce_loss(discr_real_pred, real_mask_gt) +  self.bce_loss(discr_fake_pred, mask)) / 2
-        metrics = dict(discr_real_out=discr_real_pred.mean(),
-                       discr_fake_out=discr_fake_pred.mean(),
-                       discr_real_gp=0)
-        return sum_discr_loss, metrics
-
-
-def make_discrim_loss(kind, **kwargs):
-    if kind == 'r1':
-        return NonSaturatingWithR1(**kwargs)
-    elif kind == 'bce':
-        return BCELoss(**kwargs)
-    raise ValueError(f'Unknown adversarial loss kind {kind}')

extensions-builtin/forge_preprocessor_inpaint/annotator/lama/saicinpainting/training/losses/constants.py

@@ -1,152 +0,0 @@
-weights = {"ade20k": 
-    [6.34517766497462,
-    9.328358208955224,
-    11.389521640091116,
-    16.10305958132045,
-    20.833333333333332,
-    22.22222222222222,
-    25.125628140703515,
-    43.29004329004329,
-    50.5050505050505,
-    54.6448087431694,
-    55.24861878453038,
-    60.24096385542168,
-    62.5,
-    66.2251655629139,
-    84.74576271186442,
-    90.90909090909092,
-    91.74311926605505,
-    96.15384615384616,
-    96.15384615384616,
-    97.08737864077669,
-    102.04081632653062,
-    135.13513513513513,
-    149.2537313432836,
-    153.84615384615384,
-    163.93442622950818,
-    166.66666666666666,
-    188.67924528301887,
-    192.30769230769232,
-    217.3913043478261,
-    227.27272727272725,
-    227.27272727272725,
-    227.27272727272725,
-    303.03030303030306,
-    322.5806451612903,
-    333.3333333333333,
-    370.3703703703703,
-    384.61538461538464,
-    416.6666666666667,
-    416.6666666666667,
-    434.7826086956522,
-    434.7826086956522,
-    454.5454545454545,
-    454.5454545454545,
-    500.0,
-    526.3157894736842,
-    526.3157894736842,
-    555.5555555555555,
-    555.5555555555555,
-    555.5555555555555,
-    555.5555555555555,
-    555.5555555555555,
-    555.5555555555555,
-    555.5555555555555,
-    588.2352941176471,
-    588.2352941176471,
-    588.2352941176471,
-    588.2352941176471,
-    588.2352941176471,
-    666.6666666666666,
-    666.6666666666666,
-    666.6666666666666,
-    666.6666666666666,
-    714.2857142857143,
-    714.2857142857143,
-    714.2857142857143,
-    714.2857142857143,
-    714.2857142857143,
-    769.2307692307693,
-    769.2307692307693,
-    769.2307692307693,
-    833.3333333333334,
-    833.3333333333334,
-    833.3333333333334,
-    833.3333333333334,
-    909.090909090909,
-    1000.0,
-    1111.111111111111,
-    1111.111111111111,
-    1111.111111111111,
-    1111.111111111111,
-    1111.111111111111,
-    1250.0,
-    1250.0,
-    1250.0,
-    1250.0,
-    1250.0,
-    1428.5714285714287,
-    1428.5714285714287,
-    1428.5714285714287,
-    1428.5714285714287,
-    1428.5714285714287,
-    1428.5714285714287,
-    1428.5714285714287,
-    1666.6666666666667,
-    1666.6666666666667,
-    1666.6666666666667,
-    1666.6666666666667,
-    1666.6666666666667,
-    1666.6666666666667,
-    1666.6666666666667,
-    1666.6666666666667,
-    1666.6666666666667,
-    1666.6666666666667,
-    1666.6666666666667,
-    2000.0,
-    2000.0,
-    2000.0,
-    2000.0,
-    2000.0,
-    2000.0,
-    2000.0,
-    2000.0,
-    2000.0,
-    2000.0,
-    2000.0,
-    2000.0,
-    2000.0,
-    2000.0,
-    2000.0,
-    2000.0,
-    2000.0,
-    2500.0,
-    2500.0,
-    2500.0,
-    2500.0,
-    2500.0,
-    2500.0,
-    2500.0,
-    2500.0,
-    2500.0,
-    2500.0,
-    2500.0,
-    2500.0,
-    2500.0,
-    3333.3333333333335,
-    3333.3333333333335,
-    3333.3333333333335,
-    3333.3333333333335,
-    3333.3333333333335,
-    3333.3333333333335,
-    3333.3333333333335,
-    3333.3333333333335,
-    3333.3333333333335,
-    3333.3333333333335,
-    3333.3333333333335,
-    3333.3333333333335,
-    3333.3333333333335,
-    5000.0,
-    5000.0,
-    5000.0]
-}

extensions-builtin/forge_preprocessor_inpaint/annotator/lama/saicinpainting/training/losses/distance_weighting.py

@@ -1,126 +0,0 @@
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-import torchvision
-
-from annotator.lama.saicinpainting.training.losses.perceptual import IMAGENET_STD, IMAGENET_MEAN
-
-
-def dummy_distance_weighter(real_img, pred_img, mask):
-    return mask
-
-
-def get_gauss_kernel(kernel_size, width_factor=1):
-    coords = torch.stack(torch.meshgrid(torch.arange(kernel_size),
-                                        torch.arange(kernel_size)),
-                         dim=0).float()
-    diff = torch.exp(-((coords - kernel_size // 2) ** 2).sum(0) / kernel_size / width_factor)
-    diff /= diff.sum()
-    return diff
-
-
-class BlurMask(nn.Module):
-    def __init__(self, kernel_size=5, width_factor=1):
-        super().__init__()
-        self.filter = nn.Conv2d(1, 1, kernel_size, padding=kernel_size // 2, padding_mode='replicate', bias=False)
-        self.filter.weight.data.copy_(get_gauss_kernel(kernel_size, width_factor=width_factor))
-
-    def forward(self, real_img, pred_img, mask):
-        with torch.no_grad():
-            result = self.filter(mask) * mask
-            return result
-
-
-class EmulatedEDTMask(nn.Module):
-    def __init__(self, dilate_kernel_size=5, blur_kernel_size=5, width_factor=1):
-        super().__init__()
-        self.dilate_filter = nn.Conv2d(1, 1, dilate_kernel_size, padding=dilate_kernel_size// 2, padding_mode='replicate',
-                                       bias=False)
-        self.dilate_filter.weight.data.copy_(torch.ones(1, 1, dilate_kernel_size, dilate_kernel_size, dtype=torch.float))
-        self.blur_filter = nn.Conv2d(1, 1, blur_kernel_size, padding=blur_kernel_size // 2, padding_mode='replicate', bias=False)
-        self.blur_filter.weight.data.copy_(get_gauss_kernel(blur_kernel_size, width_factor=width_factor))
-
-    def forward(self, real_img, pred_img, mask):
-        with torch.no_grad():
-            known_mask = 1 - mask
-            dilated_known_mask = (self.dilate_filter(known_mask) > 1).float()
-            result = self.blur_filter(1 - dilated_known_mask) * mask
-            return result
-
-
-class PropagatePerceptualSim(nn.Module):
-    def __init__(self, level=2, max_iters=10, temperature=500, erode_mask_size=3):
-        super().__init__()
-        vgg = torchvision.models.vgg19(pretrained=True).features
-        vgg_avg_pooling = []
-
-        for weights in vgg.parameters():
-            weights.requires_grad = False
-
-        cur_level_i = 0
-        for module in vgg.modules():
-            if module.__class__.__name__ == 'Sequential':
-                continue
-            elif module.__class__.__name__ == 'MaxPool2d':
-                vgg_avg_pooling.append(nn.AvgPool2d(kernel_size=2, stride=2, padding=0))
-            else:
-                vgg_avg_pooling.append(module)
-                if module.__class__.__name__ == 'ReLU':
-                    cur_level_i += 1
-                if cur_level_i == level:
-                    break
-
-        self.features = nn.Sequential(*vgg_avg_pooling)
-
-        self.max_iters = max_iters
-        self.temperature = temperature
-        self.do_erode = erode_mask_size > 0
-        if self.do_erode:
-            self.erode_mask = nn.Conv2d(1, 1, erode_mask_size, padding=erode_mask_size // 2, bias=False)
-            self.erode_mask.weight.data.fill_(1)
-
-    def forward(self, real_img, pred_img, mask):
-        with torch.no_grad():
-            real_img = (real_img - IMAGENET_MEAN.to(real_img)) / IMAGENET_STD.to(real_img)
-            real_feats = self.features(real_img)
-
-            vertical_sim = torch.exp(-(real_feats[:, :, 1:] - real_feats[:, :, :-1]).pow(2).sum(1, keepdim=True)
-                                     / self.temperature)
-            horizontal_sim = torch.exp(-(real_feats[:, :, :, 1:] - real_feats[:, :, :, :-1]).pow(2).sum(1, keepdim=True)
-                                       / self.temperature)
-
-            mask_scaled = F.interpolate(mask, size=real_feats.shape[-2:], mode='bilinear', align_corners=False)
-            if self.do_erode:
-                mask_scaled = (self.erode_mask(mask_scaled) > 1).float()
-
-            cur_knowness = 1 - mask_scaled
-
-            for iter_i in range(self.max_iters):
-                new_top_knowness = F.pad(cur_knowness[:, :, :-1] * vertical_sim, (0, 0, 1, 0), mode='replicate')
-                new_bottom_knowness = F.pad(cur_knowness[:, :, 1:] * vertical_sim, (0, 0, 0, 1), mode='replicate')
-
-                new_left_knowness = F.pad(cur_knowness[:, :, :, :-1] * horizontal_sim, (1, 0, 0, 0), mode='replicate')
-                new_right_knowness = F.pad(cur_knowness[:, :, :, 1:] * horizontal_sim, (0, 1, 0, 0), mode='replicate')
-
-                new_knowness = torch.stack([new_top_knowness, new_bottom_knowness,
-                                            new_left_knowness, new_right_knowness],
-                                           dim=0).max(0).values
-
-                cur_knowness = torch.max(cur_knowness, new_knowness)
-
-            cur_knowness = F.interpolate(cur_knowness, size=mask.shape[-2:], mode='bilinear')
-            result = torch.min(mask, 1 - cur_knowness)
-
-            return result
-
-
-def make_mask_distance_weighter(kind='none', **kwargs):
-    if kind == 'none':
-        return dummy_distance_weighter
-    if kind == 'blur':
-        return BlurMask(**kwargs)
-    if kind == 'edt':
-        return EmulatedEDTMask(**kwargs)
-    if kind == 'pps':
-        return PropagatePerceptualSim(**kwargs)
-    raise ValueError(f'Unknown mask distance weighter kind {kind}')

extensions-builtin/forge_preprocessor_inpaint/annotator/lama/saicinpainting/training/losses/feature_matching.py

@@ -1,33 +0,0 @@
-from typing import List
-
-import torch
-import torch.nn.functional as F
-
-
-def masked_l2_loss(pred, target, mask, weight_known, weight_missing):
-    per_pixel_l2 = F.mse_loss(pred, target, reduction='none')
-    pixel_weights = mask * weight_missing + (1 - mask) * weight_known
-    return (pixel_weights * per_pixel_l2).mean()
-
-
-def masked_l1_loss(pred, target, mask, weight_known, weight_missing):
-    per_pixel_l1 = F.l1_loss(pred, target, reduction='none')
-    pixel_weights = mask * weight_missing + (1 - mask) * weight_known
-    return (pixel_weights * per_pixel_l1).mean()
-
-
-def feature_matching_loss(fake_features: List[torch.Tensor], target_features: List[torch.Tensor], mask=None):
-    if mask is None:
-        res = torch.stack([F.mse_loss(fake_feat, target_feat)
-                           for fake_feat, target_feat in zip(fake_features, target_features)]).mean()
-    else:
-        res = 0
-        norm = 0
-        for fake_feat, target_feat in zip(fake_features, target_features):
-            cur_mask = F.interpolate(mask, size=fake_feat.shape[-2:], mode='bilinear', align_corners=False)
-            error_weights = 1 - cur_mask
-            cur_val = ((fake_feat - target_feat).pow(2) * error_weights).mean()
-            res = res + cur_val
-            norm += 1
-        res = res / norm
-    return res

extensions-builtin/forge_preprocessor_inpaint/annotator/lama/saicinpainting/training/losses/perceptual.py

@@ -1,113 +0,0 @@
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-import torchvision
-
-# from models.ade20k import ModelBuilder
-from annotator.lama.saicinpainting.utils import check_and_warn_input_range
-
-
-IMAGENET_MEAN = torch.FloatTensor([0.485, 0.456, 0.406])[None, :, None, None]
-IMAGENET_STD = torch.FloatTensor([0.229, 0.224, 0.225])[None, :, None, None]
-
-
-class PerceptualLoss(nn.Module):
-    def __init__(self, normalize_inputs=True):
-        super(PerceptualLoss, self).__init__()
-
-        self.normalize_inputs = normalize_inputs
-        self.mean_ = IMAGENET_MEAN
-        self.std_ = IMAGENET_STD
-
-        vgg = torchvision.models.vgg19(pretrained=True).features
-        vgg_avg_pooling = []
-
-        for weights in vgg.parameters():
-            weights.requires_grad = False
-
-        for module in vgg.modules():
-            if module.__class__.__name__ == 'Sequential':
-                continue
-            elif module.__class__.__name__ == 'MaxPool2d':
-                vgg_avg_pooling.append(nn.AvgPool2d(kernel_size=2, stride=2, padding=0))
-            else:
-                vgg_avg_pooling.append(module)
-
-        self.vgg = nn.Sequential(*vgg_avg_pooling)
-
-    def do_normalize_inputs(self, x):
-        return (x - self.mean_.to(x.device)) / self.std_.to(x.device)
-
-    def partial_losses(self, input, target, mask=None):
-        check_and_warn_input_range(target, 0, 1, 'PerceptualLoss target in partial_losses')
-
-        # we expect input and target to be in [0, 1] range
-        losses = []
-
-        if self.normalize_inputs:
-            features_input = self.do_normalize_inputs(input)
-            features_target = self.do_normalize_inputs(target)
-        else:
-            features_input = input
-            features_target = target
-
-        for layer in self.vgg[:30]:
-
-            features_input = layer(features_input)
-            features_target = layer(features_target)
-
-            if layer.__class__.__name__ == 'ReLU':
-                loss = F.mse_loss(features_input, features_target, reduction='none')
-
-                if mask is not None:
-                    cur_mask = F.interpolate(mask, size=features_input.shape[-2:],
-                                             mode='bilinear', align_corners=False)
-                    loss = loss * (1 - cur_mask)
-
-                loss = loss.mean(dim=tuple(range(1, len(loss.shape))))
-                losses.append(loss)
-
-        return losses
-
-    def forward(self, input, target, mask=None):
-        losses = self.partial_losses(input, target, mask=mask)
-        return torch.stack(losses).sum(dim=0)
-
-    def get_global_features(self, input):
-        check_and_warn_input_range(input, 0, 1, 'PerceptualLoss input in get_global_features')
-
-        if self.normalize_inputs:
-            features_input = self.do_normalize_inputs(input)
-        else:
-            features_input = input
-
-        features_input = self.vgg(features_input)
-        return features_input
-
-
-class ResNetPL(nn.Module):
-    def __init__(self, weight=1,
-                 weights_path=None, arch_encoder='resnet50dilated', segmentation=True):
-        super().__init__()
-        self.impl = ModelBuilder.get_encoder(weights_path=weights_path,
-                                             arch_encoder=arch_encoder,
-                                             arch_decoder='ppm_deepsup',
-                                             fc_dim=2048,
-                                             segmentation=segmentation)
-        self.impl.eval()
-        for w in self.impl.parameters():
-            w.requires_grad_(False)
-
-        self.weight = weight
-
-    def forward(self, pred, target):
-        pred = (pred - IMAGENET_MEAN.to(pred)) / IMAGENET_STD.to(pred)
-        target = (target - IMAGENET_MEAN.to(target)) / IMAGENET_STD.to(target)
-
-        pred_feats = self.impl(pred, return_feature_maps=True)
-        target_feats = self.impl(target, return_feature_maps=True)
-
-        result = torch.stack([F.mse_loss(cur_pred, cur_target)
-                              for cur_pred, cur_target
-                              in zip(pred_feats, target_feats)]).sum() * self.weight
-        return result

extensions-builtin/forge_preprocessor_inpaint/annotator/lama/saicinpainting/training/losses/segmentation.py

@@ -1,43 +0,0 @@
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-
-from .constants import weights as constant_weights
-
-
-class CrossEntropy2d(nn.Module):
-    def __init__(self, reduction="mean", ignore_label=255, weights=None, *args, **kwargs):
-        """
-        weight (Tensor, optional): a manual rescaling weight given to each class.
-            If given, has to be a Tensor of size "nclasses"
-        """
-        super(CrossEntropy2d, self).__init__()
-        self.reduction = reduction
-        self.ignore_label = ignore_label
-        self.weights = weights
-        if self.weights is not None:
-            device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
-            self.weights = torch.FloatTensor(constant_weights[weights]).to(device)
-
-    def forward(self, predict, target):
-        """
-            Args:
-                predict:(n, c, h, w)
-                target:(n, 1, h, w)
-        """
-        target = target.long()
-        assert not target.requires_grad
-        assert predict.dim() == 4, "{0}".format(predict.size())
-        assert target.dim() == 4, "{0}".format(target.size())
-        assert predict.size(0) == target.size(0), "{0} vs {1} ".format(predict.size(0), target.size(0))
-        assert target.size(1) == 1, "{0}".format(target.size(1))
-        assert predict.size(2) == target.size(2), "{0} vs {1} ".format(predict.size(2), target.size(2))
-        assert predict.size(3) == target.size(3), "{0} vs {1} ".format(predict.size(3), target.size(3))
-        target = target.squeeze(1)
-        n, c, h, w = predict.size()
-        target_mask = (target >= 0) * (target != self.ignore_label)
-        target = target[target_mask]
-        predict = predict.transpose(1, 2).transpose(2, 3).contiguous()
-        predict = predict[target_mask.view(n, h, w, 1).repeat(1, 1, 1, c)].view(-1, c)
-        loss = F.cross_entropy(predict, target, weight=self.weights, reduction=self.reduction)
-        return loss

extensions-builtin/forge_preprocessor_inpaint/annotator/lama/saicinpainting/training/losses/style_loss.py

@@ -1,155 +0,0 @@
-import torch
-import torch.nn as nn
-import torchvision.models as models
-
-
-class PerceptualLoss(nn.Module):
-    r"""
-    Perceptual loss, VGG-based
-    https://arxiv.org/abs/1603.08155
-    https://github.com/dxyang/StyleTransfer/blob/master/utils.py
-    """
-
-    def __init__(self, weights=[1.0, 1.0, 1.0, 1.0, 1.0]):
-        super(PerceptualLoss, self).__init__()
-        self.add_module('vgg', VGG19())
-        self.criterion = torch.nn.L1Loss()
-        self.weights = weights
-
-    def __call__(self, x, y):
-        # Compute features
-        x_vgg, y_vgg = self.vgg(x), self.vgg(y)
-
-        content_loss = 0.0
-        content_loss += self.weights[0] * self.criterion(x_vgg['relu1_1'], y_vgg['relu1_1'])
-        content_loss += self.weights[1] * self.criterion(x_vgg['relu2_1'], y_vgg['relu2_1'])
-        content_loss += self.weights[2] * self.criterion(x_vgg['relu3_1'], y_vgg['relu3_1'])
-        content_loss += self.weights[3] * self.criterion(x_vgg['relu4_1'], y_vgg['relu4_1'])
-        content_loss += self.weights[4] * self.criterion(x_vgg['relu5_1'], y_vgg['relu5_1'])
-
-
-        return content_loss
-
-
-class VGG19(torch.nn.Module):
-    def __init__(self):
-        super(VGG19, self).__init__()
-        features = models.vgg19(pretrained=True).features
-        self.relu1_1 = torch.nn.Sequential()
-        self.relu1_2 = torch.nn.Sequential()
-
-        self.relu2_1 = torch.nn.Sequential()
-        self.relu2_2 = torch.nn.Sequential()
-
-        self.relu3_1 = torch.nn.Sequential()
-        self.relu3_2 = torch.nn.Sequential()
-        self.relu3_3 = torch.nn.Sequential()
-        self.relu3_4 = torch.nn.Sequential()
-
-        self.relu4_1 = torch.nn.Sequential()
-        self.relu4_2 = torch.nn.Sequential()
-        self.relu4_3 = torch.nn.Sequential()
-        self.relu4_4 = torch.nn.Sequential()
-
-        self.relu5_1 = torch.nn.Sequential()
-        self.relu5_2 = torch.nn.Sequential()
-        self.relu5_3 = torch.nn.Sequential()
-        self.relu5_4 = torch.nn.Sequential()
-
-        for x in range(2):
-            self.relu1_1.add_module(str(x), features[x])
-
-        for x in range(2, 4):
-            self.relu1_2.add_module(str(x), features[x])
-
-        for x in range(4, 7):
-            self.relu2_1.add_module(str(x), features[x])
-
-        for x in range(7, 9):
-            self.relu2_2.add_module(str(x), features[x])
-
-        for x in range(9, 12):
-            self.relu3_1.add_module(str(x), features[x])
-
-        for x in range(12, 14):
-            self.relu3_2.add_module(str(x), features[x])
-
-        for x in range(14, 16):
-            self.relu3_2.add_module(str(x), features[x])
-
-        for x in range(16, 18):
-            self.relu3_4.add_module(str(x), features[x])
-
-        for x in range(18, 21):
-            self.relu4_1.add_module(str(x), features[x])
-
-        for x in range(21, 23):
-            self.relu4_2.add_module(str(x), features[x])
-
-        for x in range(23, 25):
-            self.relu4_3.add_module(str(x), features[x])
-
-        for x in range(25, 27):
-            self.relu4_4.add_module(str(x), features[x])
-
-        for x in range(27, 30):
-            self.relu5_1.add_module(str(x), features[x])
-
-        for x in range(30, 32):
-            self.relu5_2.add_module(str(x), features[x])
-
-        for x in range(32, 34):
-            self.relu5_3.add_module(str(x), features[x])
-
-        for x in range(34, 36):
-            self.relu5_4.add_module(str(x), features[x])
-
-        # don't need the gradients, just want the features
-        for param in self.parameters():
-            param.requires_grad = False
-
-    def forward(self, x):
-        relu1_1 = self.relu1_1(x)
-        relu1_2 = self.relu1_2(relu1_1)
-
-        relu2_1 = self.relu2_1(relu1_2)
-        relu2_2 = self.relu2_2(relu2_1)
-
-        relu3_1 = self.relu3_1(relu2_2)
-        relu3_2 = self.relu3_2(relu3_1)
-        relu3_3 = self.relu3_3(relu3_2)
-        relu3_4 = self.relu3_4(relu3_3)
-
-        relu4_1 = self.relu4_1(relu3_4)
-        relu4_2 = self.relu4_2(relu4_1)
-        relu4_3 = self.relu4_3(relu4_2)
-        relu4_4 = self.relu4_4(relu4_3)
-
-        relu5_1 = self.relu5_1(relu4_4)
-        relu5_2 = self.relu5_2(relu5_1)
-        relu5_3 = self.relu5_3(relu5_2)
-        relu5_4 = self.relu5_4(relu5_3)
-
-        out = {
-            'relu1_1': relu1_1,
-            'relu1_2': relu1_2,
-
-            'relu2_1': relu2_1,
-            'relu2_2': relu2_2,
-
-            'relu3_1': relu3_1,
-            'relu3_2': relu3_2,
-            'relu3_3': relu3_3,
-            'relu3_4': relu3_4,
-
-            'relu4_1': relu4_1,
-            'relu4_2': relu4_2,
-            'relu4_3': relu4_3,
-            'relu4_4': relu4_4,
-
-            'relu5_1': relu5_1,
-            'relu5_2': relu5_2,
-            'relu5_3': relu5_3,
-            'relu5_4': relu5_4,
-        }
-        return out

extensions-builtin/forge_preprocessor_inpaint/annotator/lama/saicinpainting/training/modules/__init__.py

@@ -1,31 +0,0 @@
-import logging
-
-from annotator.lama.saicinpainting.training.modules.ffc import FFCResNetGenerator
-from annotator.lama.saicinpainting.training.modules.pix2pixhd import GlobalGenerator, MultiDilatedGlobalGenerator, \
-    NLayerDiscriminator, MultidilatedNLayerDiscriminator
-
-def make_generator(config, kind, **kwargs):
-    logging.info(f'Make generator {kind}')
-
-    if kind == 'pix2pixhd_multidilated':
-        return MultiDilatedGlobalGenerator(**kwargs)
-    
-    if kind == 'pix2pixhd_global':
-        return GlobalGenerator(**kwargs)
-
-    if kind == 'ffc_resnet':
-        return FFCResNetGenerator(**kwargs)
-
-    raise ValueError(f'Unknown generator kind {kind}')
-
-
-def make_discriminator(kind, **kwargs):
-    logging.info(f'Make discriminator {kind}')
-
-    if kind == 'pix2pixhd_nlayer_multidilated':
-        return MultidilatedNLayerDiscriminator(**kwargs)
-
-    if kind == 'pix2pixhd_nlayer':
-        return NLayerDiscriminator(**kwargs)
-
-    raise ValueError(f'Unknown discriminator kind {kind}')

extensions-builtin/forge_preprocessor_inpaint/annotator/lama/saicinpainting/training/modules/base.py

@@ -1,80 +0,0 @@
-import abc
-from typing import Tuple, List
-
-import torch
-import torch.nn as nn
-
-from annotator.lama.saicinpainting.training.modules.depthwise_sep_conv import DepthWiseSeperableConv
-from annotator.lama.saicinpainting.training.modules.multidilated_conv import MultidilatedConv
-
-
-class BaseDiscriminator(nn.Module):
-    @abc.abstractmethod
-    def forward(self, x: torch.Tensor) -> Tuple[torch.Tensor, List[torch.Tensor]]:
-        """
-        Predict scores and get intermediate activations. Useful for feature matching loss
-        :return tuple (scores, list of intermediate activations)
-        """
-        raise NotImplemented()
-
-
-def get_conv_block_ctor(kind='default'):
-    if not isinstance(kind, str):
-        return kind
-    if kind == 'default':
-        return nn.Conv2d
-    if kind == 'depthwise':
-        return DepthWiseSeperableConv   
-    if kind == 'multidilated':
-        return MultidilatedConv
-    raise ValueError(f'Unknown convolutional block kind {kind}')
-
-
-def get_norm_layer(kind='bn'):
-    if not isinstance(kind, str):
-        return kind
-    if kind == 'bn':
-        return nn.BatchNorm2d
-    if kind == 'in':
-        return nn.InstanceNorm2d
-    raise ValueError(f'Unknown norm block kind {kind}')
-
-
-def get_activation(kind='tanh'):
-    if kind == 'tanh':
-        return nn.Tanh()
-    if kind == 'sigmoid':
-        return nn.Sigmoid()
-    if kind is False:
-        return nn.Identity()
-    raise ValueError(f'Unknown activation kind {kind}')
-
-
-class SimpleMultiStepGenerator(nn.Module):
-    def __init__(self, steps: List[nn.Module]):
-        super().__init__()
-        self.steps = nn.ModuleList(steps)
-
-    def forward(self, x):
-        cur_in = x
-        outs = []
-        for step in self.steps:
-            cur_out = step(cur_in)
-            outs.append(cur_out)
-            cur_in = torch.cat((cur_in, cur_out), dim=1)
-        return torch.cat(outs[::-1], dim=1)
-
-def deconv_factory(kind, ngf, mult, norm_layer, activation, max_features):
-    if kind == 'convtranspose':
-        return [nn.ConvTranspose2d(min(max_features, ngf * mult), 
-                    min(max_features, int(ngf * mult / 2)), 
-                    kernel_size=3, stride=2, padding=1, output_padding=1),
-                    norm_layer(min(max_features, int(ngf * mult / 2))), activation]
-    elif kind == 'bilinear':
-        return [nn.Upsample(scale_factor=2, mode='bilinear'),
-                DepthWiseSeperableConv(min(max_features, ngf * mult), 
-                    min(max_features, int(ngf * mult / 2)), 
-                    kernel_size=3, stride=1, padding=1), 
-                norm_layer(min(max_features, int(ngf * mult / 2))), activation]
-    else:
-        raise Exception(f"Invalid deconv kind: {kind}")

extensions-builtin/forge_preprocessor_inpaint/annotator/lama/saicinpainting/training/modules/depthwise_sep_conv.py

@@ -1,17 +0,0 @@
-import torch
-import torch.nn as nn
-
-class DepthWiseSeperableConv(nn.Module):
-    def __init__(self, in_dim, out_dim, *args, **kwargs):
-        super().__init__()
-        if 'groups' in kwargs:
-            # ignoring groups for Depthwise Sep Conv
-            del kwargs['groups']
-        
-        self.depthwise = nn.Conv2d(in_dim, in_dim, *args, groups=in_dim, **kwargs)
-        self.pointwise = nn.Conv2d(in_dim, out_dim, kernel_size=1)
-        
-    def forward(self, x):
-        out = self.depthwise(x)
-        out = self.pointwise(out)
-        return out

extensions-builtin/forge_preprocessor_inpaint/annotator/lama/saicinpainting/training/modules/fake_fakes.py

@@ -1,47 +0,0 @@
-import torch
-from kornia import SamplePadding
-from kornia.augmentation import RandomAffine, CenterCrop
-
-
-class FakeFakesGenerator:
-    def __init__(self, aug_proba=0.5, img_aug_degree=30, img_aug_translate=0.2):
-        self.grad_aug = RandomAffine(degrees=360,
-                                     translate=0.2,
-                                     padding_mode=SamplePadding.REFLECTION,
-                                     keepdim=False,
-                                     p=1)
-        self.img_aug = RandomAffine(degrees=img_aug_degree,
-                                    translate=img_aug_translate,
-                                    padding_mode=SamplePadding.REFLECTION,
-                                    keepdim=True,
-                                    p=1)
-        self.aug_proba = aug_proba
-
-    def __call__(self, input_images, masks):
-        blend_masks = self._fill_masks_with_gradient(masks)
-        blend_target = self._make_blend_target(input_images)
-        result = input_images * (1 - blend_masks) + blend_target * blend_masks
-        return result, blend_masks
-
-    def _make_blend_target(self, input_images):
-        batch_size = input_images.shape[0]
-        permuted = input_images[torch.randperm(batch_size)]
-        augmented = self.img_aug(input_images)
-        is_aug = (torch.rand(batch_size, device=input_images.device)[:, None, None, None] < self.aug_proba).float()
-        result = augmented * is_aug + permuted * (1 - is_aug)
-        return result
-
-    def _fill_masks_with_gradient(self, masks):
-        batch_size, _, height, width = masks.shape
-        grad = torch.linspace(0, 1, steps=width * 2, device=masks.device, dtype=masks.dtype) \
-            .view(1, 1, 1, -1).expand(batch_size, 1, height * 2, width * 2)
-        grad = self.grad_aug(grad)
-        grad = CenterCrop((height, width))(grad)
-        grad *= masks
-
-        grad_for_min = grad + (1 - masks) * 10
-        grad -= grad_for_min.view(batch_size, -1).min(-1).values[:, None, None, None]
-        grad /= grad.view(batch_size, -1).max(-1).values[:, None, None, None] + 1e-6
-        grad.clamp_(min=0, max=1)
-
-        return grad

extensions-builtin/forge_preprocessor_inpaint/annotator/lama/saicinpainting/training/modules/ffc.py

@@ -1,600 +0,0 @@
-# Fast Fourier Convolution NeurIPS 2020
-# original implementation https://github.com/pkumivision/FFC/blob/main/model_zoo/ffc.py
-# paper https://proceedings.neurips.cc/paper/2020/file/2fd5d41ec6cfab47e32164d5624269b1-Paper.pdf
-
-import numpy as np
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-
-from annotator.lama.saicinpainting.training.modules.base import get_activation, BaseDiscriminator
-from annotator.lama.saicinpainting.training.modules.spatial_transform import LearnableSpatialTransformWrapper
-from annotator.lama.saicinpainting.training.modules.squeeze_excitation import SELayer
-from annotator.lama.saicinpainting.utils import get_shape
-
-
-class FFCSE_block(nn.Module):
-
-    def __init__(self, channels, ratio_g):
-        super(FFCSE_block, self).__init__()
-        in_cg = int(channels * ratio_g)
-        in_cl = channels - in_cg
-        r = 16
-
-        self.avgpool = nn.AdaptiveAvgPool2d((1, 1))
-        self.conv1 = nn.Conv2d(channels, channels // r,
-                               kernel_size=1, bias=True)
-        self.relu1 = nn.ReLU(inplace=True)
-        self.conv_a2l = None if in_cl == 0 else nn.Conv2d(
-            channels // r, in_cl, kernel_size=1, bias=True)
-        self.conv_a2g = None if in_cg == 0 else nn.Conv2d(
-            channels // r, in_cg, kernel_size=1, bias=True)
-        self.sigmoid = nn.Sigmoid()
-
-    def forward(self, x):
-        x = x if type(x) is tuple else (x, 0)
-        id_l, id_g = x
-
-        # Determine the device of id_l
-        x_device = id_l.device if torch.is_tensor(id_l) else (id_g.device if torch.is_tensor(id_g) else 'cpu')
-
-        # Move layers to the same device
-        self.avgpool = self.avgpool.to(x_device)
-        self.conv1 = self.conv1.to(x_device)
-        if self.conv_a2l is not None:
-            self.conv_a2l = self.conv_a2l.to(x_device)
-        if self.conv_a2g is not None:
-            self.conv_a2g = self.conv_a2g.to(x_device)
-
-        x = id_l if type(id_g) is int else torch.cat([id_l, id_g], dim=1)
-        x = self.avgpool(x)
-        x = self.relu1(self.conv1(x))
-
-        x_l = 0 if self.conv_a2l is None else id_l * self.sigmoid(self.conv_a2l(x))
-        x_g = 0 if self.conv_a2g is None else id_g * self.sigmoid(self.conv_a2g(x))
-        return x_l, x_g
-
-
-class FourierUnit(nn.Module):
-
-    def __init__(self, in_channels, out_channels, groups=1, spatial_scale_factor=None, spatial_scale_mode='bilinear',
-                 spectral_pos_encoding=False, use_se=False, se_kwargs=None, ffc3d=False, fft_norm='ortho'):
-        # bn_layer not used
-        super(FourierUnit, self).__init__()
-        self.groups = groups
-
-        self.conv_layer = torch.nn.Conv2d(in_channels=in_channels * 2 + (2 if spectral_pos_encoding else 0),
-                                          out_channels=out_channels * 2,
-                                          kernel_size=1, stride=1, padding=0, groups=self.groups, bias=False)
-        self.bn = torch.nn.BatchNorm2d(out_channels * 2)
-        self.relu = torch.nn.ReLU(inplace=True)
-
-        # squeeze and excitation block
-        self.use_se = use_se
-        if use_se:
-            if se_kwargs is None:
-                se_kwargs = {}
-            self.se = SELayer(self.conv_layer.in_channels, **se_kwargs)
-
-        self.spatial_scale_factor = spatial_scale_factor
-        self.spatial_scale_mode = spatial_scale_mode
-        self.spectral_pos_encoding = spectral_pos_encoding
-        self.ffc3d = ffc3d
-        self.fft_norm = fft_norm
-
-    def forward(self, x):
-        # Determine the device of x
-        x_device = x.device
-
-        # Move layers to the same device
-        self.conv_layer = self.conv_layer.to(x_device)
-        self.bn = self.bn.to(x_device)
-
-        batch = x.shape[0]
-
-        if self.spatial_scale_factor is not None:
-            orig_size = x.shape[-2:]
-            x = F.interpolate(x, scale_factor=self.spatial_scale_factor, mode=self.spatial_scale_mode, align_corners=False)
-
-        r_size = x.size()
-        # (batch, c, h, w/2+1, 2)
-        fft_dim = (-3, -2, -1) if self.ffc3d else (-2, -1)
-        ffted = torch.fft.rfftn(x, dim=fft_dim, norm=self.fft_norm)
-        ffted = torch.stack((ffted.real, ffted.imag), dim=-1)
-        ffted = ffted.permute(0, 1, 4, 2, 3).contiguous()  # (batch, c, 2, h, w/2+1)
-        ffted = ffted.view((batch, -1,) + ffted.size()[3:])
-
-        if self.spectral_pos_encoding:
-            height, width = ffted.shape[-2:]
-            coords_vert = torch.linspace(0, 1, height)[None, None, :, None].expand(batch, 1, height, width).to(x_device)
-            coords_hor = torch.linspace(0, 1, width)[None, None, None, :].expand(batch, 1, height, width).to(x_device)
-            ffted = torch.cat((coords_vert, coords_hor, ffted), dim=1)
-
-        if self.use_se:
-            ffted = self.se(ffted)
-
-        ffted = self.conv_layer(ffted)  # (batch, c*2, h, w/2+1)
-        ffted = self.relu(self.bn(ffted))
-
-        ffted = ffted.view((batch, -1, 2,) + ffted.size()[2:]).permute(
-            0, 1, 3, 4, 2).contiguous()  # (batch,c, t, h, w/2+1, 2)
-        ffted = torch.complex(ffted[..., 0], ffted[..., 1])
-
-        ifft_shape_slice = x.shape[-3:] if self.ffc3d else x.shape[-2:]
-        output = torch.fft.irfftn(ffted, s=ifft_shape_slice, dim=fft_dim, norm=self.fft_norm)
-
-        if self.spatial_scale_factor is not None:
-            output = F.interpolate(output, size=orig_size, mode=self.spatial_scale_mode, align_corners=False)
-
-        return output
-
-
-class SeparableFourierUnit(nn.Module):
-
-    def __init__(self, in_channels, out_channels, groups=1, kernel_size=3):
-        # bn_layer not used
-        super(SeparableFourierUnit, self).__init__()
-        self.groups = groups
-        row_out_channels = out_channels // 2
-        col_out_channels = out_channels - row_out_channels
-        self.row_conv = torch.nn.Conv2d(in_channels=in_channels * 2,
-                                        out_channels=row_out_channels * 2,
-                                        kernel_size=(kernel_size, 1),  # kernel size is always like this, but the data will be transposed
-                                        stride=1, padding=(kernel_size // 2, 0),
-                                        padding_mode='reflect',
-                                        groups=self.groups, bias=False)
-        self.col_conv = torch.nn.Conv2d(in_channels=in_channels * 2,
-                                        out_channels=col_out_channels * 2,
-                                        kernel_size=(kernel_size, 1),  # kernel size is always like this, but the data will be transposed
-                                        stride=1, padding=(kernel_size // 2, 0),
-                                        padding_mode='reflect',
-                                        groups=self.groups, bias=False)
-        self.row_bn = torch.nn.BatchNorm2d(row_out_channels * 2)
-        self.col_bn = torch.nn.BatchNorm2d(col_out_channels * 2)
-        self.relu = torch.nn.ReLU(inplace=True)
-
-    def process_branch(self, x, conv, bn):
-        # Determine the device of x
-        x_device = x.device
-
-        # Move layers to the same device
-        conv = conv.to(x_device)
-        bn = bn.to(x_device)
-
-        batch = x.shape[0]
-
-        r_size = x.size()
-        ffted = torch.fft.rfft(x, norm="ortho")
-        ffted = torch.stack((ffted.real, ffted.imag), dim=-1)
-        ffted = ffted.permute(0, 1, 4, 2, 3).contiguous()  # (batch, c, 2, h, w/2+1)
-        ffted = ffted.view((batch, -1,) + ffted.size()[3:])
-
-        ffted = self.relu(bn(conv(ffted)))
-
-        ffted = ffted.view((batch, -1, 2,) + ffted.size()[2:]).permute(
-            0, 1, 3, 4, 2).contiguous()  # (batch,c, t, h, w/2+1, 2)
-        ffted = torch.complex(ffted[..., 0], ffted[..., 1])
-
-        output = torch.fft.irfft(ffted, s=x.shape[-1:], norm="ortho")
-        return output
-
-    def forward(self, x):
-        # Determine the device of x
-        x_device = x.device
-
-        # Move layers to the same device
-        self.row_conv = self.row_conv.to(x_device)
-        self.col_conv = self.col_conv.to(x_device)
-        self.row_bn = self.row_bn.to(x_device)
-        self.col_bn = self.col_bn.to(x_device)
-
-        rowwise = self.process_branch(x, self.row_conv, self.row_bn)
-        colwise = self.process_branch(x.permute(0, 1, 3, 2), self.col_conv, self.col_bn).permute(0, 1, 3, 2)
-        out = torch.cat((rowwise, colwise), dim=1)
-        return out
-
-
-class SpectralTransform(nn.Module):
-
-    def __init__(self, in_channels, out_channels, stride=1, groups=1, enable_lfu=True, separable_fu=False, **fu_kwargs):
-        # bn_layer not used
-        super(SpectralTransform, self).__init__()
-        self.enable_lfu = enable_lfu
-        if stride == 2:
-            self.downsample = nn.AvgPool2d(kernel_size=(2, 2), stride=2)
-        else:
-            self.downsample = nn.Identity()
-
-        self.stride = stride
-        self.conv1 = nn.Sequential(
-            nn.Conv2d(in_channels, out_channels //
-                      2, kernel_size=1, groups=groups, bias=False),
-            nn.BatchNorm2d(out_channels // 2),
-            nn.ReLU(inplace=True)
-        )
-        fu_class = SeparableFourierUnit if separable_fu else FourierUnit
-        self.fu = fu_class(
-            out_channels // 2, out_channels // 2, groups, **fu_kwargs)
-        if self.enable_lfu:
-            self.lfu = fu_class(
-                out_channels // 2, out_channels // 2, groups)
-        self.conv2 = torch.nn.Conv2d(
-            out_channels // 2, out_channels, kernel_size=1, groups=groups, bias=False)
-
-    def forward(self, x):
-        # Determine the device of x
-        x_device = x.device
-
-        # Move layers to the same device
-        self.downsample = self.downsample.to(x_device)
-        self.conv1 = self.conv1.to(x_device)
-        self.fu = self.fu.to(x_device)
-        if self.enable_lfu:
-            self.lfu = self.lfu.to(x_device)
-        self.conv2 = self.conv2.to(x_device)
-
-        x = self.downsample(x)
-        x = self.conv1(x)
-        output = self.fu(x)
-
-        if self.enable_lfu:
-            n, c, h, w = x.shape
-            split_no = 2
-            split_s = h // split_no
-            xs = torch.cat(torch.split(
-                x[:, :c // 4], split_s, dim=-2), dim=1).contiguous()
-            xs = torch.cat(torch.split(xs, split_s, dim=-1),
-                           dim=1).contiguous()
-            xs = self.lfu(xs)
-            xs = xs.repeat(1, 1, split_no, split_no).contiguous()
-        else:
-            xs = 0
-
-        output = self.conv2(x + output + xs)
-
-        return output
-
-
-class FFC(nn.Module):
-
-    def __init__(self, in_channels, out_channels, kernel_size,
-                 ratio_gin, ratio_gout, stride=1, padding=0,
-                 dilation=1, groups=1, bias=False, enable_lfu=True,
-                 padding_type='reflect', gated=False, **spectral_kwargs):
-        super(FFC, self).__init__()
-
-        assert stride == 1 or stride == 2, "Stride should be 1 or 2."
-        self.stride = stride
-
-        in_cg = int(in_channels * ratio_gin)
-        in_cl = in_channels - in_cg
-        out_cg = int(out_channels * ratio_gout)
-        out_cl = out_channels - out_cg
-        #groups_g = 1 if groups == 1 else int(groups * ratio_gout)
-        #groups_l = 1 if groups == 1 else groups - groups_g
-
-        self.ratio_gin = ratio_gin
-        self.ratio_gout = ratio_gout
-        self.global_in_num = in_cg
-
-        module = nn.Identity if in_cl == 0 or out_cl == 0 else nn.Conv2d
-        self.convl2l = module(in_cl, out_cl, kernel_size,
-                              stride, padding, dilation, groups, bias, padding_mode=padding_type)
-        module = nn.Identity if in_cl == 0 or out_cg == 0 else nn.Conv2d
-        self.convl2g = module(in_cl, out_cg, kernel_size,
-                              stride, padding, dilation, groups, bias, padding_mode=padding_type)
-        module = nn.Identity if in_cg == 0 or out_cl == 0 else nn.Conv2d
-        self.convg2l = module(in_cg, out_cl, kernel_size,
-                              stride, padding, dilation, groups, bias, padding_mode=padding_type)
-        module = nn.Identity if in_cg == 0 or out_cg == 0 else SpectralTransform
-        self.convg2g = module(
-            in_cg, out_cg, stride, 1 if groups == 1 else groups // 2, enable_lfu, **spectral_kwargs)
-
-        self.gated = gated
-        module = nn.Identity if in_cg == 0 or out_cl == 0 or not self.gated else nn.Conv2d
-        self.gate = module(in_channels, 2, 1)
-
-    def forward(self, x):
-        x_l, x_g = x if type(x) is tuple else (x, 0)
-        
-        # Determine the device of x_l
-        x_device = x_l.device
-        
-        # Ensure x_g is on the same device as x_l
-        if torch.is_tensor(x_g):
-            x_g = x_g.to(x_device)
-
-        # Move all convolution layers to the same device
-        self.convl2l = self.convl2l.to(x_device)
-        self.convl2g = self.convl2g.to(x_device)
-        self.convg2l = self.convg2l.to(x_device)
-        self.convg2g = self.convg2g.to(x_device)
-
-        if self.gated:
-            self.gate = self.gate.to(x_device)
-
-        out_xl, out_xg = 0, 0
-
-        if self.gated:
-            total_input_parts = [x_l]
-            if torch.is_tensor(x_g):
-                total_input_parts.append(x_g)
-            total_input = torch.cat(total_input_parts, dim=1)
-
-            gates = torch.sigmoid(self.gate(total_input))
-            g2l_gate, l2g_gate = gates.chunk(2, dim=1)
-        else:
-            g2l_gate, l2g_gate = 1, 1
-
-        if self.ratio_gout != 1:
-            out_xl = self.convl2l(x_l) + self.convg2l(x_g) * g2l_gate
-        if self.ratio_gout != 0:
-            out_xg = self.convl2g(x_l) * l2g_gate + self.convg2g(x_g)
-
-        return out_xl, out_xg
-
-
-class FFC_BN_ACT(nn.Module):
-
-    def __init__(self, in_channels, out_channels,
-                 kernel_size, ratio_gin, ratio_gout,
-                 stride=1, padding=0, dilation=1, groups=1, bias=False,
-                 norm_layer=nn.BatchNorm2d, activation_layer=nn.Identity,
-                 padding_type='reflect',
-                 enable_lfu=True, **kwargs):
-        super(FFC_BN_ACT, self).__init__()
-        self.ffc = FFC(in_channels, out_channels, kernel_size,
-                       ratio_gin, ratio_gout, stride, padding, dilation,
-                       groups, bias, enable_lfu, padding_type=padding_type, **kwargs)
-        lnorm = nn.Identity if ratio_gout == 1 else norm_layer
-        gnorm = nn.Identity if ratio_gout == 0 else norm_layer
-        global_channels = int(out_channels * ratio_gout)
-        self.bn_l = lnorm(out_channels - global_channels)
-        self.bn_g = gnorm(global_channels)
-
-        lact = nn.Identity if ratio_gout == 1 else activation_layer
-        gact = nn.Identity if ratio_gout == 0 else activation_layer
-        self.act_l = lact(inplace=True)
-        self.act_g = gact(inplace=True)
-
-    def forward(self, x):
-        # Determine the device of x
-        x_device = x[0].device if isinstance(x, tuple) else x.device
-
-        # Move layers to the same device
-        self.ffc = self.ffc.to(x_device)
-        self.bn_l = self.bn_l.to(x_device)
-        self.bn_g = self.bn_g.to(x_device)
-        self.act_l = self.act_l.to(x_device)
-        self.act_g = self.act_g.to(x_device)
-
-        x_l, x_g = self.ffc(x)
-        x_l = self.act_l(self.bn_l(x_l))
-        x_g = self.act_g(self.bn_g(x_g))
-        return x_l, x_g
-
-
-class FFCResnetBlock(nn.Module):
-    def __init__(self, dim, padding_type, norm_layer, activation_layer=nn.ReLU, dilation=1,
-                 spatial_transform_kwargs=None, inline=False, **conv_kwargs):
-        super().__init__()
-        self.conv1 = FFC_BN_ACT(dim, dim, kernel_size=3, padding=dilation, dilation=dilation,
-                                norm_layer=norm_layer,
-                                activation_layer=activation_layer,
-                                padding_type=padding_type,
-                                **conv_kwargs)
-        self.conv2 = FFC_BN_ACT(dim, dim, kernel_size=3, padding=dilation, dilation=dilation,
-                                norm_layer=norm_layer,
-                                activation_layer=activation_layer,
-                                padding_type=padding_type,
-                                **conv_kwargs)
-        if spatial_transform_kwargs is not None:
-            self.conv1 = LearnableSpatialTransformWrapper(self.conv1, **spatial_transform_kwargs)
-            self.conv2 = LearnableSpatialTransformWrapper(self.conv2, **spatial_transform_kwargs)
-        self.inline = inline
-
-    def forward(self, x):
-        if self.inline:
-            x_l, x_g = x[:, :-self.conv1.ffc.global_in_num], x[:, -self.conv1.ffc.global_in_num:]
-        else:
-            x_l, x_g = x if type(x) is tuple else (x, 0)
-
-        # Determine the device of x_l
-        x_device = x_l.device
-        
-        # Ensure x_g is on the same device as x_l
-        if torch.is_tensor(x_g):
-            x_g = x_g.to(x_device)
-
-        # Move conv layers to the same device
-        self.conv1 = self.conv1.to(x_device)
-        self.conv2 = self.conv2.to(x_device)
-
-        id_l, id_g = x_l, x_g
-
-        x_l, x_g = self.conv1((x_l, x_g))
-        x_l, x_g = self.conv2((x_l, x_g))
-
-        x_l, x_g = id_l + x_l, id_g + x_g
-        out = x_l, x_g
-        if self.inline:
-            out = torch.cat(out, dim=1)
-        return out
-
-
-class ConcatTupleLayer(nn.Module):
-    def forward(self, x):
-        assert isinstance(x, tuple)
-        x_l, x_g = x
-        assert torch.is_tensor(x_l) or torch.is_tensor(x_g)
-        if not torch.is_tensor(x_g):
-            return x_l
-        return torch.cat(x, dim=1).to(x_l.device)
-
-
-class FFCResNetGenerator(nn.Module):
-    def __init__(self, input_nc, output_nc, ngf=64, n_downsampling=3, n_blocks=9, norm_layer=nn.BatchNorm2d,
-                 padding_type='reflect', activation_layer=nn.ReLU,
-                 up_norm_layer=nn.BatchNorm2d, up_activation=nn.ReLU(True),
-                 init_conv_kwargs={}, downsample_conv_kwargs={}, resnet_conv_kwargs={},
-                 spatial_transform_layers=None, spatial_transform_kwargs={},
-                 add_out_act=True, max_features=1024, out_ffc=False, out_ffc_kwargs={}):
-        assert (n_blocks >= 0)
-        super().__init__()
-
-        model = [nn.ReflectionPad2d(3),
-                 FFC_BN_ACT(input_nc, ngf, kernel_size=7, padding=0, norm_layer=norm_layer,
-                            activation_layer=activation_layer, **init_conv_kwargs)]
-
-        ### downsample
-        for i in range(n_downsampling):
-            mult = 2 ** i
-            if i == n_downsampling - 1:
-                cur_conv_kwargs = dict(downsample_conv_kwargs)
-                cur_conv_kwargs['ratio_gout'] = resnet_conv_kwargs.get('ratio_gin', 0)
-            else:
-                cur_conv_kwargs = downsample_conv_kwargs
-            model += [FFC_BN_ACT(min(max_features, ngf * mult),
-                                 min(max_features, ngf * mult * 2),
-                                 kernel_size=3, stride=2, padding=1,
-                                 norm_layer=norm_layer,
-                                 activation_layer=activation_layer,
-                                 **cur_conv_kwargs)]
-
-        mult = 2 ** n_downsampling
-        feats_num_bottleneck = min(max_features, ngf * mult)
-
-        ### resnet blocks
-        for i in range(n_blocks):
-            cur_resblock = FFCResnetBlock(feats_num_bottleneck, padding_type=padding_type, activation_layer=activation_layer,
-                                          norm_layer=norm_layer, **resnet_conv_kwargs)
-            if spatial_transform_layers is not None and i in spatial_transform_layers:
-                cur_resblock = LearnableSpatialTransformWrapper(cur_resblock, **spatial_transform_kwargs)
-            model += [cur_resblock]
-
-        model += [ConcatTupleLayer()]
-
-        ### upsample
-        for i in range(n_downsampling):
-            mult = 2 ** (n_downsampling - i)
-            model += [nn.ConvTranspose2d(min(max_features, ngf * mult),
-                                         min(max_features, int(ngf * mult / 2)),
-                                         kernel_size=3, stride=2, padding=1, output_padding=1),
-                      up_norm_layer(min(max_features, int(ngf * mult / 2))),
-                      up_activation]
-
-        if out_ffc:
-            model += [FFCResnetBlock(ngf, padding_type=padding_type, activation_layer=activation_layer,
-                                     norm_layer=norm_layer, inline=True, **out_ffc_kwargs)]
-
-        model += [nn.ReflectionPad2d(3),
-                  nn.Conv2d(ngf, output_nc, kernel_size=7, padding=0)]
-        if add_out_act:
-            model.append(get_activation('tanh' if add_out_act is True else add_out_act))
-        self.model = nn.Sequential(*model)
-
-    def to(self, *args, **kwargs):
-        # First, call the parent class's to() method
-        self = super().to(*args, **kwargs)
-       
-        # Then, explicitly move all submodules
-        for module in self.modules():
-            if isinstance(module, (nn.Conv2d, nn.ConvTranspose2d, nn.BatchNorm2d)):
-                module.to(*args, **kwargs)
-       
-        return self
-
-    def forward(self, input):
-        # Find the first layer with a 'weight' attribute
-        for layer in self.model:
-            if hasattr(layer, 'weight'):
-                device = layer.weight.device
-                break
-        else:
-            # If no layer with 'weight' is found, use the device of the first parameter
-            device = next(self.parameters()).device
-
-        # Ensure input is on the same device as the model
-        input = input.to(device)
-        return self.model(input)
-
-
-class FFCNLayerDiscriminator(BaseDiscriminator):
-    def __init__(self, input_nc, ndf=64, n_layers=3, norm_layer=nn.BatchNorm2d, max_features=512,
-                 init_conv_kwargs={}, conv_kwargs={}):
-        super().__init__()
-        self.n_layers = n_layers
-
-        def _act_ctor(inplace=True):
-            return nn.LeakyReLU(negative_slope=0.2, inplace=inplace)
-
-        kw = 3
-        padw = int(np.ceil((kw-1.0)/2))
-        sequence = [[FFC_BN_ACT(input_nc, ndf, kernel_size=kw, padding=padw, norm_layer=norm_layer,
-                                activation_layer=_act_ctor, **init_conv_kwargs)]]
-
-        nf = ndf
-        for n in range(1, n_layers):
-            nf_prev = nf
-            nf = min(nf * 2, max_features)
-
-            cur_model = [
-                FFC_BN_ACT(nf_prev, nf,
-                           kernel_size=kw, stride=2, padding=padw,
-                           norm_layer=norm_layer,
-                           activation_layer=_act_ctor,
-                           **conv_kwargs)
-            ]
-            sequence.append(cur_model)
-
-        nf_prev = nf
-        nf = min(nf * 2, 512)
-
-        cur_model = [
-            FFC_BN_ACT(nf_prev, nf,
-                       kernel_size=kw, stride=1, padding=padw,
-                       norm_layer=norm_layer,
-                       activation_layer=lambda *args, **kwargs: nn.LeakyReLU(*args, negative_slope=0.2, **kwargs),
-                       **conv_kwargs),
-            ConcatTupleLayer()
-        ]
-        sequence.append(cur_model)
-
-        sequence += [[nn.Conv2d(nf, 1, kernel_size=kw, stride=1, padding=padw)]]
-
-        for n in range(len(sequence)):
-            setattr(self, 'model'+str(n), nn.Sequential(*sequence[n]))
-
-    def get_all_activations(self, x):
-        res = [x]
-        for n in range(self.n_layers + 2):
-            model = getattr(self, 'model' + str(n))
-            res.append(model(res[-1]))
-        return res[1:]
-
-    def forward(self, x):
-        # Find the device of the first parameter
-        device = next(self.parameters()).device
-        x = x.to(device)
-        act = self.get_all_activations(x)
-        feats = []
-        for out in act[:-1]:
-            if isinstance(out, tuple):
-                if torch.is_tensor(out[1]):
-                    out = torch.cat(out, dim=1)
-                else:
-                    out = out[0]
-            feats.append(out)
-        return act[-1], feats
-    
-    def to(self, *args, **kwargs):
-        # First, call the parent class's to() method
-        self = super().to(*args, **kwargs)
-       
-        # Then, explicitly move all submodules
-        for module in self.modules():
-            if isinstance(module, (nn.Conv2d, nn.ConvTranspose2d, nn.BatchNorm2d)):
-                module.to(*args, **kwargs)
-       
-        return self
-    

extensions-builtin/forge_preprocessor_inpaint/annotator/lama/saicinpainting/training/modules/multidilated_conv.py

@@ -1,98 +0,0 @@
-import torch
-import torch.nn as nn
-import random
-from annotator.lama.saicinpainting.training.modules.depthwise_sep_conv import DepthWiseSeperableConv
-
-class MultidilatedConv(nn.Module):
-    def __init__(self, in_dim, out_dim, kernel_size, dilation_num=3, comb_mode='sum', equal_dim=True,
-                 shared_weights=False, padding=1, min_dilation=1, shuffle_in_channels=False, use_depthwise=False, **kwargs):
-        super().__init__()
-        convs = []
-        self.equal_dim = equal_dim
-        assert comb_mode in ('cat_out', 'sum', 'cat_in', 'cat_both'), comb_mode
-        if comb_mode in ('cat_out', 'cat_both'):
-            self.cat_out = True
-            if equal_dim:
-                assert out_dim % dilation_num == 0
-                out_dims = [out_dim // dilation_num] * dilation_num
-                self.index = sum([[i + j * (out_dims[0]) for j in range(dilation_num)] for i in range(out_dims[0])], [])
-            else:
-                out_dims = [out_dim // 2 ** (i + 1) for i in range(dilation_num - 1)]
-                out_dims.append(out_dim - sum(out_dims))
-                index = []
-                starts = [0] + out_dims[:-1]
-                lengths = [out_dims[i] // out_dims[-1] for i in range(dilation_num)]
-                for i in range(out_dims[-1]):
-                    for j in range(dilation_num):
-                        index += list(range(starts[j], starts[j] + lengths[j]))
-                        starts[j] += lengths[j]
-                self.index = index
-                assert(len(index) == out_dim)
-            self.out_dims = out_dims
-        else:
-            self.cat_out = False
-            self.out_dims = [out_dim] * dilation_num
-
-        if comb_mode in ('cat_in', 'cat_both'):
-            if equal_dim:
-                assert in_dim % dilation_num == 0
-                in_dims = [in_dim // dilation_num] * dilation_num
-            else:
-                in_dims = [in_dim // 2 ** (i + 1) for i in range(dilation_num - 1)]
-                in_dims.append(in_dim - sum(in_dims))
-            self.in_dims = in_dims
-            self.cat_in = True
-        else:
-            self.cat_in = False
-            self.in_dims = [in_dim] * dilation_num
-
-        conv_type = DepthWiseSeperableConv if use_depthwise else nn.Conv2d
-        dilation = min_dilation
-        for i in range(dilation_num):
-            if isinstance(padding, int):
-                cur_padding = padding * dilation
-            else:
-                cur_padding = padding[i]
-            convs.append(conv_type(
-                self.in_dims[i], self.out_dims[i], kernel_size, padding=cur_padding, dilation=dilation, **kwargs
-            ))
-            if i > 0 and shared_weights:
-                convs[-1].weight = convs[0].weight
-                convs[-1].bias = convs[0].bias
-            dilation *= 2
-        self.convs = nn.ModuleList(convs)
-
-        self.shuffle_in_channels = shuffle_in_channels
-        if self.shuffle_in_channels:
-            # shuffle list as shuffling of tensors is nondeterministic
-            in_channels_permute = list(range(in_dim))
-            random.shuffle(in_channels_permute)
-            # save as buffer so it is saved and loaded with checkpoint
-            self.register_buffer('in_channels_permute', torch.tensor(in_channels_permute))
-
-    def forward(self, x):
-        if self.shuffle_in_channels:
-            x = x[:, self.in_channels_permute]
-
-        outs = []
-        if self.cat_in:
-            if self.equal_dim:
-                x = x.chunk(len(self.convs), dim=1)
-            else:
-                new_x = []
-                start = 0
-                for dim in self.in_dims:
-                    new_x.append(x[:, start:start+dim])
-                    start += dim
-                x = new_x
-        for i, conv in enumerate(self.convs):
-            if self.cat_in:
-                input = x[i]
-            else:
-                input = x
-            outs.append(conv(input))
-        if self.cat_out:
-            out = torch.cat(outs, dim=1)[:, self.index]
-        else:
-            out = sum(outs)
-        return out

extensions-builtin/forge_preprocessor_inpaint/annotator/lama/saicinpainting/training/modules/multiscale.py

@@ -1,244 +0,0 @@
-from typing import List, Tuple, Union, Optional
-
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-
-from annotator.lama.saicinpainting.training.modules.base import get_conv_block_ctor, get_activation
-from annotator.lama.saicinpainting.training.modules.pix2pixhd import ResnetBlock
-
-
-class ResNetHead(nn.Module):
-    def __init__(self, input_nc, ngf=64, n_downsampling=3, n_blocks=9, norm_layer=nn.BatchNorm2d,
-                 padding_type='reflect', conv_kind='default', activation=nn.ReLU(True)):
-        assert (n_blocks >= 0)
-        super(ResNetHead, self).__init__()
-
-        conv_layer = get_conv_block_ctor(conv_kind)
-
-        model = [nn.ReflectionPad2d(3),
-                 conv_layer(input_nc, ngf, kernel_size=7, padding=0),
-                 norm_layer(ngf),
-                 activation]
-
-        ### downsample
-        for i in range(n_downsampling):
-            mult = 2 ** i
-            model += [conv_layer(ngf * mult, ngf * mult * 2, kernel_size=3, stride=2, padding=1),
-                      norm_layer(ngf * mult * 2),
-                      activation]
-
-        mult = 2 ** n_downsampling
-
-        ### resnet blocks
-        for i in range(n_blocks):
-            model += [ResnetBlock(ngf * mult, padding_type=padding_type, activation=activation, norm_layer=norm_layer,
-                                  conv_kind=conv_kind)]
-
-        self.model = nn.Sequential(*model)
-
-    def forward(self, input):
-        return self.model(input)
-
-
-class ResNetTail(nn.Module):
-    def __init__(self, output_nc, ngf=64, n_downsampling=3, n_blocks=9, norm_layer=nn.BatchNorm2d,
-                 padding_type='reflect', conv_kind='default', activation=nn.ReLU(True),
-                 up_norm_layer=nn.BatchNorm2d, up_activation=nn.ReLU(True), add_out_act=False, out_extra_layers_n=0,
-                 add_in_proj=None):
-        assert (n_blocks >= 0)
-        super(ResNetTail, self).__init__()
-
-        mult = 2 ** n_downsampling
-
-        model = []
-
-        if add_in_proj is not None:
-            model.append(nn.Conv2d(add_in_proj, ngf * mult, kernel_size=1))
-
-        ### resnet blocks
-        for i in range(n_blocks):
-            model += [ResnetBlock(ngf * mult, padding_type=padding_type, activation=activation, norm_layer=norm_layer,
-                                  conv_kind=conv_kind)]
-
-        ### upsample
-        for i in range(n_downsampling):
-            mult = 2 ** (n_downsampling - i)
-            model += [nn.ConvTranspose2d(ngf * mult, int(ngf * mult / 2), kernel_size=3, stride=2, padding=1,
-                                         output_padding=1),
-                      up_norm_layer(int(ngf * mult / 2)),
-                      up_activation]
-        self.model = nn.Sequential(*model)
-
-        out_layers = []
-        for _ in range(out_extra_layers_n):
-            out_layers += [nn.Conv2d(ngf, ngf, kernel_size=1, padding=0),
-                           up_norm_layer(ngf),
-                           up_activation]
-        out_layers += [nn.ReflectionPad2d(3),
-                       nn.Conv2d(ngf, output_nc, kernel_size=7, padding=0)]
-
-        if add_out_act:
-            out_layers.append(get_activation('tanh' if add_out_act is True else add_out_act))
-
-        self.out_proj = nn.Sequential(*out_layers)
-
-    def forward(self, input, return_last_act=False):
-        features = self.model(input)
-        out = self.out_proj(features)
-        if return_last_act:
-            return out, features
-        else:
-            return out
-
-
-class MultiscaleResNet(nn.Module):
-    def __init__(self, input_nc, output_nc, ngf=64, n_downsampling=2, n_blocks_head=2, n_blocks_tail=6, n_scales=3,
-                 norm_layer=nn.BatchNorm2d, padding_type='reflect', conv_kind='default', activation=nn.ReLU(True),
-                 up_norm_layer=nn.BatchNorm2d, up_activation=nn.ReLU(True), add_out_act=False, out_extra_layers_n=0,
-                 out_cumulative=False, return_only_hr=False):
-        super().__init__()
-
-        self.heads = nn.ModuleList([ResNetHead(input_nc, ngf=ngf, n_downsampling=n_downsampling,
-                                               n_blocks=n_blocks_head, norm_layer=norm_layer, padding_type=padding_type,
-                                               conv_kind=conv_kind, activation=activation)
-                                    for i in range(n_scales)])
-        tail_in_feats = ngf * (2 ** n_downsampling) + ngf
-        self.tails = nn.ModuleList([ResNetTail(output_nc,
-                                               ngf=ngf, n_downsampling=n_downsampling,
-                                               n_blocks=n_blocks_tail, norm_layer=norm_layer, padding_type=padding_type,
-                                               conv_kind=conv_kind, activation=activation, up_norm_layer=up_norm_layer,
-                                               up_activation=up_activation, add_out_act=add_out_act,
-                                               out_extra_layers_n=out_extra_layers_n,
-                                               add_in_proj=None if (i == n_scales - 1) else tail_in_feats)
-                                    for i in range(n_scales)])
-
-        self.out_cumulative = out_cumulative
-        self.return_only_hr = return_only_hr
-
-    @property
-    def num_scales(self):
-        return len(self.heads)
-
-    def forward(self, ms_inputs: List[torch.Tensor], smallest_scales_num: Optional[int] = None) \
-        -> Union[torch.Tensor, List[torch.Tensor]]:
-        """
-        :param ms_inputs: List of inputs of different resolutions from HR to LR
-        :param smallest_scales_num: int or None, number of smallest scales to take at input
-        :return: Depending on return_only_hr:
-            True: Only the most HR output
-            False: List of outputs of different resolutions from HR to LR
-        """
-        if smallest_scales_num is None:
-            assert len(self.heads) == len(ms_inputs), (len(self.heads), len(ms_inputs), smallest_scales_num)
-            smallest_scales_num = len(self.heads)
-        else:
-            assert smallest_scales_num == len(ms_inputs) <= len(self.heads), (len(self.heads), len(ms_inputs), smallest_scales_num)
-
-        cur_heads = self.heads[-smallest_scales_num:]
-        ms_features = [cur_head(cur_inp) for cur_head, cur_inp in zip(cur_heads, ms_inputs)]
-
-        all_outputs = []
-        prev_tail_features = None
-        for i in range(len(ms_features)):
-            scale_i = -i - 1
-
-            cur_tail_input = ms_features[-i - 1]
-            if prev_tail_features is not None:
-                if prev_tail_features.shape != cur_tail_input.shape:
-                    prev_tail_features = F.interpolate(prev_tail_features, size=cur_tail_input.shape[2:],
-                                                       mode='bilinear', align_corners=False)
-                cur_tail_input = torch.cat((cur_tail_input, prev_tail_features), dim=1)
-
-            cur_out, cur_tail_feats = self.tails[scale_i](cur_tail_input, return_last_act=True)
-
-            prev_tail_features = cur_tail_feats
-            all_outputs.append(cur_out)
-
-        if self.out_cumulative:
-            all_outputs_cum = [all_outputs[0]]
-            for i in range(1, len(ms_features)):
-                cur_out = all_outputs[i]
-                cur_out_cum = cur_out + F.interpolate(all_outputs_cum[-1], size=cur_out.shape[2:],
-                                                      mode='bilinear', align_corners=False)
-                all_outputs_cum.append(cur_out_cum)
-            all_outputs = all_outputs_cum
-
-        if self.return_only_hr:
-            return all_outputs[-1]
-        else:
-            return all_outputs[::-1]
-
-
-class MultiscaleDiscriminatorSimple(nn.Module):
-    def __init__(self, ms_impl):
-        super().__init__()
-        self.ms_impl = nn.ModuleList(ms_impl)
-
-    @property
-    def num_scales(self):
-        return len(self.ms_impl)
-
-    def forward(self, ms_inputs: List[torch.Tensor], smallest_scales_num: Optional[int] = None) \
-            -> List[Tuple[torch.Tensor, List[torch.Tensor]]]:
-        """
-        :param ms_inputs: List of inputs of different resolutions from HR to LR
-        :param smallest_scales_num: int or None, number of smallest scales to take at input
-        :return: List of pairs (prediction, features) for different resolutions from HR to LR
-        """
-        if smallest_scales_num is None:
-            assert len(self.ms_impl) == len(ms_inputs), (len(self.ms_impl), len(ms_inputs), smallest_scales_num)
-            smallest_scales_num = len(self.heads)
-        else:
-            assert smallest_scales_num == len(ms_inputs) <= len(self.ms_impl), \
-                (len(self.ms_impl), len(ms_inputs), smallest_scales_num)
-
-        return [cur_discr(cur_input) for cur_discr, cur_input in zip(self.ms_impl[-smallest_scales_num:], ms_inputs)]
-
-
-class SingleToMultiScaleInputMixin:
-    def forward(self, x: torch.Tensor) -> List:
-        orig_height, orig_width = x.shape[2:]
-        factors = [2 ** i for i in range(self.num_scales)]
-        ms_inputs = [F.interpolate(x, size=(orig_height // f, orig_width // f), mode='bilinear', align_corners=False)
-                     for f in factors]
-        return super().forward(ms_inputs)
-
-
-class GeneratorMultiToSingleOutputMixin:
-    def forward(self, x):
-        return super().forward(x)[0]
-
-
-class DiscriminatorMultiToSingleOutputMixin:
-    def forward(self, x):
-        out_feat_tuples = super().forward(x)
-        return out_feat_tuples[0][0], [f for _, flist in out_feat_tuples for f in flist]
-
-
-class DiscriminatorMultiToSingleOutputStackedMixin:
-    def __init__(self, *args, return_feats_only_levels=None, **kwargs):
-        super().__init__(*args, **kwargs)
-        self.return_feats_only_levels = return_feats_only_levels
-
-    def forward(self, x):
-        out_feat_tuples = super().forward(x)
-        outs = [out for out, _ in out_feat_tuples]
-        scaled_outs = [outs[0]] + [F.interpolate(cur_out, size=outs[0].shape[-2:],
-                                                 mode='bilinear', align_corners=False)
-                                   for cur_out in outs[1:]]
-        out = torch.cat(scaled_outs, dim=1)
-        if self.return_feats_only_levels is not None:
-            feat_lists = [out_feat_tuples[i][1] for i in self.return_feats_only_levels]
-        else:
-            feat_lists = [flist for _, flist in out_feat_tuples]
-        feats = [f for flist in feat_lists for f in flist]
-        return out, feats
-
-
-class MultiscaleDiscrSingleInput(SingleToMultiScaleInputMixin, DiscriminatorMultiToSingleOutputStackedMixin, MultiscaleDiscriminatorSimple):
-    pass
-
-
-class MultiscaleResNetSingle(GeneratorMultiToSingleOutputMixin, SingleToMultiScaleInputMixin, MultiscaleResNet):
-    pass

extensions-builtin/forge_preprocessor_inpaint/annotator/lama/saicinpainting/training/modules/pix2pixhd.py

@@ -1,669 +0,0 @@
-# original: https://github.com/NVIDIA/pix2pixHD/blob/master/models/networks.py
-import collections
-from functools import partial
-import functools
-import logging
-from collections import defaultdict
-
-import numpy as np
-import torch.nn as nn
-
-from annotator.lama.saicinpainting.training.modules.base import BaseDiscriminator, deconv_factory, get_conv_block_ctor, get_norm_layer, get_activation
-from annotator.lama.saicinpainting.training.modules.ffc import FFCResnetBlock
-from annotator.lama.saicinpainting.training.modules.multidilated_conv import MultidilatedConv
-
-class DotDict(defaultdict):
-    # https://stackoverflow.com/questions/2352181/how-to-use-a-dot-to-access-members-of-dictionary
-    """dot.notation access to dictionary attributes"""
-    __getattr__ = defaultdict.get
-    __setattr__ = defaultdict.__setitem__
-    __delattr__ = defaultdict.__delitem__
-
-class Identity(nn.Module):
-    def __init__(self):
-        super().__init__()
-
-    def forward(self, x):
-        return x
-
-
-class ResnetBlock(nn.Module):
-    def __init__(self, dim, padding_type, norm_layer, activation=nn.ReLU(True), use_dropout=False, conv_kind='default',
-                 dilation=1, in_dim=None, groups=1, second_dilation=None):
-        super(ResnetBlock, self).__init__()
-        self.in_dim = in_dim
-        self.dim = dim
-        if second_dilation is None:
-            second_dilation = dilation
-        self.conv_block = self.build_conv_block(dim, padding_type, norm_layer, activation, use_dropout,
-                                                conv_kind=conv_kind, dilation=dilation, in_dim=in_dim, groups=groups,
-                                                second_dilation=second_dilation)
-
-        if self.in_dim is not None:
-            self.input_conv = nn.Conv2d(in_dim, dim, 1)
-
-        self.out_channnels = dim
-
-    def build_conv_block(self, dim, padding_type, norm_layer, activation, use_dropout, conv_kind='default',
-                         dilation=1, in_dim=None, groups=1, second_dilation=1):
-        conv_layer = get_conv_block_ctor(conv_kind)
-
-        conv_block = []
-        p = 0
-        if padding_type == 'reflect':
-            conv_block += [nn.ReflectionPad2d(dilation)]
-        elif padding_type == 'replicate':
-            conv_block += [nn.ReplicationPad2d(dilation)]
-        elif padding_type == 'zero':
-            p = dilation
-        else:
-            raise NotImplementedError('padding [%s] is not implemented' % padding_type)
-
-        if in_dim is None:
-            in_dim = dim
-
-        conv_block += [conv_layer(in_dim, dim, kernel_size=3, padding=p, dilation=dilation),
-                       norm_layer(dim),
-                       activation]
-        if use_dropout:
-            conv_block += [nn.Dropout(0.5)]
-
-        p = 0
-        if padding_type == 'reflect':
-            conv_block += [nn.ReflectionPad2d(second_dilation)]
-        elif padding_type == 'replicate':
-            conv_block += [nn.ReplicationPad2d(second_dilation)]
-        elif padding_type == 'zero':
-            p = second_dilation
-        else:
-            raise NotImplementedError('padding [%s] is not implemented' % padding_type)
-        conv_block += [conv_layer(dim, dim, kernel_size=3, padding=p, dilation=second_dilation, groups=groups),
-                       norm_layer(dim)]
-
-        return nn.Sequential(*conv_block)
-
-    def forward(self, x):
-        x_before = x
-        if self.in_dim is not None:
-            x = self.input_conv(x)
-        out = x + self.conv_block(x_before)
-        return out
-
-class ResnetBlock5x5(nn.Module):
-    def __init__(self, dim, padding_type, norm_layer, activation=nn.ReLU(True), use_dropout=False, conv_kind='default',
-                 dilation=1, in_dim=None, groups=1, second_dilation=None):
-        super(ResnetBlock5x5, self).__init__()
-        self.in_dim = in_dim
-        self.dim = dim
-        if second_dilation is None:
-            second_dilation = dilation
-        self.conv_block = self.build_conv_block(dim, padding_type, norm_layer, activation, use_dropout,
-                                                conv_kind=conv_kind, dilation=dilation, in_dim=in_dim, groups=groups,
-                                                second_dilation=second_dilation)
-
-        if self.in_dim is not None:
-            self.input_conv = nn.Conv2d(in_dim, dim, 1)
-
-        self.out_channnels = dim
-
-    def build_conv_block(self, dim, padding_type, norm_layer, activation, use_dropout, conv_kind='default',
-                         dilation=1, in_dim=None, groups=1, second_dilation=1):
-        conv_layer = get_conv_block_ctor(conv_kind)
-
-        conv_block = []
-        p = 0
-        if padding_type == 'reflect':
-            conv_block += [nn.ReflectionPad2d(dilation * 2)]
-        elif padding_type == 'replicate':
-            conv_block += [nn.ReplicationPad2d(dilation * 2)]
-        elif padding_type == 'zero':
-            p = dilation * 2
-        else:
-            raise NotImplementedError('padding [%s] is not implemented' % padding_type)
-
-        if in_dim is None:
-            in_dim = dim
-
-        conv_block += [conv_layer(in_dim, dim, kernel_size=5, padding=p, dilation=dilation),
-                       norm_layer(dim),
-                       activation]
-        if use_dropout:
-            conv_block += [nn.Dropout(0.5)]
-
-        p = 0
-        if padding_type == 'reflect':
-            conv_block += [nn.ReflectionPad2d(second_dilation * 2)]
-        elif padding_type == 'replicate':
-            conv_block += [nn.ReplicationPad2d(second_dilation * 2)]
-        elif padding_type == 'zero':
-            p = second_dilation * 2
-        else:
-            raise NotImplementedError('padding [%s] is not implemented' % padding_type)
-        conv_block += [conv_layer(dim, dim, kernel_size=5, padding=p, dilation=second_dilation, groups=groups),
-                       norm_layer(dim)]
-
-        return nn.Sequential(*conv_block)
-
-    def forward(self, x):
-        x_before = x
-        if self.in_dim is not None:
-            x = self.input_conv(x)
-        out = x + self.conv_block(x_before)
-        return out
-
-
-class MultidilatedResnetBlock(nn.Module):
-    def __init__(self, dim, padding_type, conv_layer, norm_layer, activation=nn.ReLU(True), use_dropout=False):
-        super().__init__()
-        self.conv_block = self.build_conv_block(dim, padding_type, conv_layer, norm_layer, activation, use_dropout)
-
-    def build_conv_block(self, dim, padding_type, conv_layer, norm_layer, activation, use_dropout, dilation=1):
-        conv_block = []
-        conv_block += [conv_layer(dim, dim, kernel_size=3, padding_mode=padding_type),
-                       norm_layer(dim),
-                       activation]
-        if use_dropout:
-            conv_block += [nn.Dropout(0.5)]
-
-        conv_block += [conv_layer(dim, dim, kernel_size=3, padding_mode=padding_type),
-                       norm_layer(dim)]
-
-        return nn.Sequential(*conv_block)
-
-    def forward(self, x):
-        out = x + self.conv_block(x)
-        return out
-
-
-class MultiDilatedGlobalGenerator(nn.Module):
-    def __init__(self, input_nc, output_nc, ngf=64, n_downsampling=3,
-                 n_blocks=3, norm_layer=nn.BatchNorm2d,
-                 padding_type='reflect', conv_kind='default',
-                 deconv_kind='convtranspose', activation=nn.ReLU(True),
-                 up_norm_layer=nn.BatchNorm2d, affine=None, up_activation=nn.ReLU(True),
-                 add_out_act=True, max_features=1024, multidilation_kwargs={},
-                 ffc_positions=None, ffc_kwargs={}):
-        assert (n_blocks >= 0)
-        super().__init__()
-
-        conv_layer = get_conv_block_ctor(conv_kind)
-        resnet_conv_layer = functools.partial(get_conv_block_ctor('multidilated'), **multidilation_kwargs)
-        norm_layer = get_norm_layer(norm_layer)
-        if affine is not None:
-            norm_layer = partial(norm_layer, affine=affine)
-        up_norm_layer = get_norm_layer(up_norm_layer)
-        if affine is not None:
-            up_norm_layer = partial(up_norm_layer, affine=affine)
-
-        model = [nn.ReflectionPad2d(3),
-                 conv_layer(input_nc, ngf, kernel_size=7, padding=0),
-                 norm_layer(ngf),
-                 activation]
-
-        identity = Identity()
-        ### downsample
-        for i in range(n_downsampling):
-            mult = 2 ** i
-
-            model += [conv_layer(min(max_features, ngf * mult),
-                                    min(max_features, ngf * mult * 2),
-                                    kernel_size=3, stride=2, padding=1),
-                        norm_layer(min(max_features, ngf * mult * 2)),
-                        activation]
-
-        mult = 2 ** n_downsampling
-        feats_num_bottleneck = min(max_features, ngf * mult)
-
-        ### resnet blocks
-        for i in range(n_blocks):
-            if ffc_positions is not None and i in ffc_positions:
-                model += [FFCResnetBlock(feats_num_bottleneck, padding_type, norm_layer, activation_layer=nn.ReLU,
-                                         inline=True, **ffc_kwargs)]
-            model += [MultidilatedResnetBlock(feats_num_bottleneck, padding_type=padding_type,
-                                              conv_layer=resnet_conv_layer, activation=activation,
-                                              norm_layer=norm_layer)]
-
-        ### upsample
-        for i in range(n_downsampling):
-            mult = 2 ** (n_downsampling - i)
-            model += deconv_factory(deconv_kind, ngf, mult, up_norm_layer, up_activation, max_features)
-        model += [nn.ReflectionPad2d(3),
-                  nn.Conv2d(ngf, output_nc, kernel_size=7, padding=0)]
-        if add_out_act:
-            model.append(get_activation('tanh' if add_out_act is True else add_out_act))
-        self.model = nn.Sequential(*model)
-
-    def forward(self, input):
-        return self.model(input)
-
-class ConfigGlobalGenerator(nn.Module):
-    def __init__(self, input_nc, output_nc, ngf=64, n_downsampling=3,
-                 n_blocks=3, norm_layer=nn.BatchNorm2d,
-                 padding_type='reflect', conv_kind='default',
-                 deconv_kind='convtranspose', activation=nn.ReLU(True),
-                 up_norm_layer=nn.BatchNorm2d, affine=None, up_activation=nn.ReLU(True),
-                 add_out_act=True, max_features=1024,
-                 manual_block_spec=[],
-                 resnet_block_kind='multidilatedresnetblock',
-                 resnet_conv_kind='multidilated',
-                 resnet_dilation=1,
-                 multidilation_kwargs={}):
-        assert (n_blocks >= 0)
-        super().__init__()
-
-        conv_layer = get_conv_block_ctor(conv_kind)
-        resnet_conv_layer = functools.partial(get_conv_block_ctor(resnet_conv_kind), **multidilation_kwargs)
-        norm_layer = get_norm_layer(norm_layer)
-        if affine is not None:
-            norm_layer = partial(norm_layer, affine=affine)
-        up_norm_layer = get_norm_layer(up_norm_layer)
-        if affine is not None:
-            up_norm_layer = partial(up_norm_layer, affine=affine)
-
-        model = [nn.ReflectionPad2d(3),
-                 conv_layer(input_nc, ngf, kernel_size=7, padding=0),
-                 norm_layer(ngf),
-                 activation]
-
-        identity = Identity()
-
-        ### downsample
-        for i in range(n_downsampling):
-            mult = 2 ** i
-            model += [conv_layer(min(max_features, ngf * mult),
-                                    min(max_features, ngf * mult * 2),
-                                    kernel_size=3, stride=2, padding=1),
-                        norm_layer(min(max_features, ngf * mult * 2)),
-                        activation]
-
-        mult = 2 ** n_downsampling
-        feats_num_bottleneck = min(max_features, ngf * mult)
-
-        if len(manual_block_spec) == 0:
-            manual_block_spec = [
-                DotDict(lambda : None, {
-                    'n_blocks': n_blocks,
-                    'use_default': True})
-            ]
-
-        ### resnet blocks
-        for block_spec in manual_block_spec:
-            def make_and_add_blocks(model, block_spec):
-                block_spec = DotDict(lambda : None, block_spec)
-                if not block_spec.use_default:
-                    resnet_conv_layer = functools.partial(get_conv_block_ctor(block_spec.resnet_conv_kind), **block_spec.multidilation_kwargs)
-                    resnet_conv_kind = block_spec.resnet_conv_kind
-                    resnet_block_kind = block_spec.resnet_block_kind
-                    if block_spec.resnet_dilation is not None:
-                        resnet_dilation = block_spec.resnet_dilation
-                for i in range(block_spec.n_blocks):
-                    if resnet_block_kind == "multidilatedresnetblock":
-                        model += [MultidilatedResnetBlock(feats_num_bottleneck, padding_type=padding_type,
-                                                        conv_layer=resnet_conv_layer, activation=activation,
-                                                        norm_layer=norm_layer)]
-                    if resnet_block_kind == "resnetblock":                                            
-                        model += [ResnetBlock(ngf * mult, padding_type=padding_type, activation=activation, norm_layer=norm_layer,
-                                            conv_kind=resnet_conv_kind)]
-                    if resnet_block_kind == "resnetblock5x5":                                            
-                        model += [ResnetBlock5x5(ngf * mult, padding_type=padding_type, activation=activation, norm_layer=norm_layer,
-                                            conv_kind=resnet_conv_kind)]
-                    if resnet_block_kind == "resnetblockdwdil":
-                        model += [ResnetBlock(ngf * mult, padding_type=padding_type, activation=activation, norm_layer=norm_layer,
-                                            conv_kind=resnet_conv_kind, dilation=resnet_dilation, second_dilation=resnet_dilation)]
-            make_and_add_blocks(model, block_spec)
-        
-        ### upsample
-        for i in range(n_downsampling):
-            mult = 2 ** (n_downsampling - i)
-            model += deconv_factory(deconv_kind, ngf, mult, up_norm_layer, up_activation, max_features)
-        model += [nn.ReflectionPad2d(3),
-                  nn.Conv2d(ngf, output_nc, kernel_size=7, padding=0)]
-        if add_out_act:
-            model.append(get_activation('tanh' if add_out_act is True else add_out_act))
-        self.model = nn.Sequential(*model)
-
-    def forward(self, input):
-        return self.model(input)
-
-
-def make_dil_blocks(dilated_blocks_n, dilation_block_kind, dilated_block_kwargs):
-    blocks = []
-    for i in range(dilated_blocks_n):
-        if dilation_block_kind == 'simple':
-            blocks.append(ResnetBlock(**dilated_block_kwargs, dilation=2 ** (i + 1)))
-        elif dilation_block_kind == 'multi':
-            blocks.append(MultidilatedResnetBlock(**dilated_block_kwargs))
-        else:
-            raise ValueError(f'dilation_block_kind could not be "{dilation_block_kind}"')
-    return blocks
-
-
-class GlobalGenerator(nn.Module):
-    def __init__(self, input_nc, output_nc, ngf=64, n_downsampling=3, n_blocks=9, norm_layer=nn.BatchNorm2d,
-                 padding_type='reflect', conv_kind='default', activation=nn.ReLU(True),
-                 up_norm_layer=nn.BatchNorm2d, affine=None,
-                 up_activation=nn.ReLU(True), dilated_blocks_n=0, dilated_blocks_n_start=0,
-                 dilated_blocks_n_middle=0,
-                 add_out_act=True,
-                 max_features=1024, is_resblock_depthwise=False,
-                 ffc_positions=None, ffc_kwargs={}, dilation=1, second_dilation=None,
-                 dilation_block_kind='simple', multidilation_kwargs={}):
-        assert (n_blocks >= 0)
-        super().__init__()
-
-        conv_layer = get_conv_block_ctor(conv_kind)
-        norm_layer = get_norm_layer(norm_layer)
-        if affine is not None:
-            norm_layer = partial(norm_layer, affine=affine)
-        up_norm_layer = get_norm_layer(up_norm_layer)
-        if affine is not None:
-            up_norm_layer = partial(up_norm_layer, affine=affine)
-
-        if ffc_positions is not None:
-            ffc_positions = collections.Counter(ffc_positions)
-
-        model = [nn.ReflectionPad2d(3),
-                 conv_layer(input_nc, ngf, kernel_size=7, padding=0),
-                 norm_layer(ngf),
-                 activation]
-
-        identity = Identity()
-        ### downsample
-        for i in range(n_downsampling):
-            mult = 2 ** i
-
-            model += [conv_layer(min(max_features, ngf * mult),
-                                min(max_features, ngf * mult * 2),
-                                kernel_size=3, stride=2, padding=1),
-                        norm_layer(min(max_features, ngf * mult * 2)),
-                        activation]
-
-        mult = 2 ** n_downsampling
-        feats_num_bottleneck = min(max_features, ngf * mult)
-
-        dilated_block_kwargs = dict(dim=feats_num_bottleneck, padding_type=padding_type,
-                                    activation=activation, norm_layer=norm_layer)
-        if dilation_block_kind == 'simple':
-            dilated_block_kwargs['conv_kind'] = conv_kind
-        elif dilation_block_kind == 'multi':
-            dilated_block_kwargs['conv_layer'] = functools.partial(
-                get_conv_block_ctor('multidilated'), **multidilation_kwargs)
-
-        # dilated blocks at the start of the bottleneck sausage
-        if dilated_blocks_n_start is not None and dilated_blocks_n_start > 0:
-            model += make_dil_blocks(dilated_blocks_n_start, dilation_block_kind, dilated_block_kwargs)
-
-        # resnet blocks
-        for i in range(n_blocks):
-            # dilated blocks at the middle of the bottleneck sausage
-            if i == n_blocks // 2 and dilated_blocks_n_middle is not None and dilated_blocks_n_middle > 0:
-                model += make_dil_blocks(dilated_blocks_n_middle, dilation_block_kind, dilated_block_kwargs)
-            
-            if ffc_positions is not None and i in ffc_positions:
-                for _ in range(ffc_positions[i]):  # same position can occur more than once
-                    model += [FFCResnetBlock(feats_num_bottleneck, padding_type, norm_layer, activation_layer=nn.ReLU,
-                                             inline=True, **ffc_kwargs)]
-
-            if is_resblock_depthwise:
-                resblock_groups = feats_num_bottleneck
-            else:
-                resblock_groups = 1
-
-            model += [ResnetBlock(feats_num_bottleneck, padding_type=padding_type, activation=activation,
-                                    norm_layer=norm_layer, conv_kind=conv_kind, groups=resblock_groups,
-                                    dilation=dilation, second_dilation=second_dilation)]
-            
-
-        # dilated blocks at the end of the bottleneck sausage
-        if dilated_blocks_n is not None and dilated_blocks_n > 0:
-            model += make_dil_blocks(dilated_blocks_n, dilation_block_kind, dilated_block_kwargs)
-
-        # upsample
-        for i in range(n_downsampling):
-            mult = 2 ** (n_downsampling - i)
-            model += [nn.ConvTranspose2d(min(max_features, ngf * mult),
-                                         min(max_features, int(ngf * mult / 2)),
-                                         kernel_size=3, stride=2, padding=1, output_padding=1),
-                      up_norm_layer(min(max_features, int(ngf * mult / 2))),
-                      up_activation]
-        model += [nn.ReflectionPad2d(3),
-                  nn.Conv2d(ngf, output_nc, kernel_size=7, padding=0)]
-        if add_out_act:
-            model.append(get_activation('tanh' if add_out_act is True else add_out_act))
-        self.model = nn.Sequential(*model)
-
-    def forward(self, input):
-        return self.model(input)
-
-
-class GlobalGeneratorGated(GlobalGenerator):
-    def __init__(self, *args, **kwargs):
-        real_kwargs=dict(
-            conv_kind='gated_bn_relu',
-            activation=nn.Identity(),
-            norm_layer=nn.Identity
-        )
-        real_kwargs.update(kwargs)
-        super().__init__(*args, **real_kwargs)
-
-
-class GlobalGeneratorFromSuperChannels(nn.Module):
-    def __init__(self, input_nc, output_nc, n_downsampling, n_blocks, super_channels, norm_layer="bn", padding_type='reflect', add_out_act=True):
-        super().__init__()
-        self.n_downsampling = n_downsampling
-        norm_layer = get_norm_layer(norm_layer)
-        if type(norm_layer) == functools.partial:
-            use_bias = (norm_layer.func == nn.InstanceNorm2d)
-        else:
-            use_bias = (norm_layer == nn.InstanceNorm2d)
-
-        channels = self.convert_super_channels(super_channels)
-        self.channels = channels
-
-        model = [nn.ReflectionPad2d(3),
-                 nn.Conv2d(input_nc, channels[0], kernel_size=7, padding=0, bias=use_bias),
-                 norm_layer(channels[0]),
-                 nn.ReLU(True)]
-
-        for i in range(n_downsampling):  # add downsampling layers
-            mult = 2 ** i
-            model += [nn.Conv2d(channels[0+i], channels[1+i], kernel_size=3, stride=2, padding=1, bias=use_bias),
-                      norm_layer(channels[1+i]),
-                      nn.ReLU(True)]
-
-        mult = 2 ** n_downsampling
-
-        n_blocks1 = n_blocks // 3
-        n_blocks2 = n_blocks1
-        n_blocks3 = n_blocks - n_blocks1 - n_blocks2
-
-        for i in range(n_blocks1):
-            c = n_downsampling
-            dim = channels[c]
-            model += [ResnetBlock(dim, padding_type=padding_type, norm_layer=norm_layer)]
-
-        for i in range(n_blocks2):
-            c = n_downsampling+1
-            dim = channels[c]
-            kwargs = {}
-            if i == 0:
-                kwargs = {"in_dim": channels[c-1]}
-            model += [ResnetBlock(dim, padding_type=padding_type, norm_layer=norm_layer, **kwargs)]
-
-        for i in range(n_blocks3):
-            c = n_downsampling+2
-            dim = channels[c]
-            kwargs = {}
-            if i == 0:
-                kwargs = {"in_dim": channels[c-1]}
-            model += [ResnetBlock(dim, padding_type=padding_type, norm_layer=norm_layer, **kwargs)]
-
-        for i in range(n_downsampling):  # add upsampling layers
-            mult = 2 ** (n_downsampling - i)
-            model += [nn.ConvTranspose2d(channels[n_downsampling+3+i],
-                                           channels[n_downsampling+3+i+1],
-                                           kernel_size=3, stride=2,
-                                           padding=1, output_padding=1,
-                                           bias=use_bias),
-                      norm_layer(channels[n_downsampling+3+i+1]),
-                      nn.ReLU(True)]
-        model += [nn.ReflectionPad2d(3)]
-        model += [nn.Conv2d(channels[2*n_downsampling+3], output_nc, kernel_size=7, padding=0)]
-
-        if add_out_act:
-            model.append(get_activation('tanh' if add_out_act is True else add_out_act))
-        self.model = nn.Sequential(*model)
-
-    def convert_super_channels(self, super_channels):
-        n_downsampling = self.n_downsampling
-        result = []
-        cnt = 0
-
-        if n_downsampling == 2:
-            N1 = 10
-        elif n_downsampling == 3:
-            N1 = 13
-        else:
-            raise NotImplementedError
-
-        for i in range(0, N1):
-            if i in [1,4,7,10]:
-                channel = super_channels[cnt] * (2 ** cnt)
-                config = {'channel': channel}
-                result.append(channel)
-                logging.info(f"Downsample channels {result[-1]}")
-                cnt += 1
-
-        for i in range(3):
-            for counter, j in enumerate(range(N1 + i * 3, N1 + 3 + i * 3)):
-                if len(super_channels) == 6:
-                    channel = super_channels[3] * 4
-                else:
-                    channel = super_channels[i + 3] * 4
-                config = {'channel': channel}
-                if counter == 0:
-                    result.append(channel)
-                    logging.info(f"Bottleneck channels {result[-1]}")
-        cnt = 2
-
-        for i in range(N1+9, N1+21):
-            if i in [22, 25,28]:
-                cnt -= 1
-                if len(super_channels) == 6:
-                    channel = super_channels[5 - cnt] * (2 ** cnt)
-                else:
-                    channel = super_channels[7 - cnt] * (2 ** cnt)
-                result.append(int(channel))
-                logging.info(f"Upsample channels {result[-1]}")
-        return result
-
-    def forward(self, input):
-        return self.model(input)
-
-
-# Defines the PatchGAN discriminator with the specified arguments.
-class NLayerDiscriminator(BaseDiscriminator):
-    def __init__(self, input_nc, ndf=64, n_layers=3, norm_layer=nn.BatchNorm2d,):
-        super().__init__()
-        self.n_layers = n_layers
-
-        kw = 4
-        padw = int(np.ceil((kw-1.0)/2))
-        sequence = [[nn.Conv2d(input_nc, ndf, kernel_size=kw, stride=2, padding=padw),
-                     nn.LeakyReLU(0.2, True)]]
-
-        nf = ndf
-        for n in range(1, n_layers):
-            nf_prev = nf
-            nf = min(nf * 2, 512)
-
-            cur_model = []
-            cur_model += [
-                nn.Conv2d(nf_prev, nf, kernel_size=kw, stride=2, padding=padw),
-                norm_layer(nf),
-                nn.LeakyReLU(0.2, True)
-            ]
-            sequence.append(cur_model)
-
-        nf_prev = nf
-        nf = min(nf * 2, 512)
-
-        cur_model = []
-        cur_model += [
-            nn.Conv2d(nf_prev, nf, kernel_size=kw, stride=1, padding=padw),
-            norm_layer(nf),
-            nn.LeakyReLU(0.2, True)
-        ]
-        sequence.append(cur_model)
-
-        sequence += [[nn.Conv2d(nf, 1, kernel_size=kw, stride=1, padding=padw)]]
-
-        for n in range(len(sequence)):
-            setattr(self, 'model'+str(n), nn.Sequential(*sequence[n]))
-
-    def get_all_activations(self, x):
-        res = [x]
-        for n in range(self.n_layers + 2):
-            model = getattr(self, 'model' + str(n))
-            res.append(model(res[-1]))
-        return res[1:]
-
-    def forward(self, x):
-        act = self.get_all_activations(x)
-        return act[-1], act[:-1]
-
-
-class MultidilatedNLayerDiscriminator(BaseDiscriminator):
-    def __init__(self, input_nc, ndf=64, n_layers=3, norm_layer=nn.BatchNorm2d, multidilation_kwargs={}):
-        super().__init__()
-        self.n_layers = n_layers
-
-        kw = 4
-        padw = int(np.ceil((kw-1.0)/2))
-        sequence = [[nn.Conv2d(input_nc, ndf, kernel_size=kw, stride=2, padding=padw),
-                     nn.LeakyReLU(0.2, True)]]
-
-        nf = ndf
-        for n in range(1, n_layers):
-            nf_prev = nf
-            nf = min(nf * 2, 512)
-
-            cur_model = []
-            cur_model += [
-                MultidilatedConv(nf_prev, nf, kernel_size=kw, stride=2, padding=[2, 3], **multidilation_kwargs),
-                norm_layer(nf),
-                nn.LeakyReLU(0.2, True)
-            ]
-            sequence.append(cur_model)
-
-        nf_prev = nf
-        nf = min(nf * 2, 512)
-
-        cur_model = []
-        cur_model += [
-            nn.Conv2d(nf_prev, nf, kernel_size=kw, stride=1, padding=padw),
-            norm_layer(nf),
-            nn.LeakyReLU(0.2, True)
-        ]
-        sequence.append(cur_model)
-
-        sequence += [[nn.Conv2d(nf, 1, kernel_size=kw, stride=1, padding=padw)]]
-
-        for n in range(len(sequence)):
-            setattr(self, 'model'+str(n), nn.Sequential(*sequence[n]))
-
-    def get_all_activations(self, x):
-        res = [x]
-        for n in range(self.n_layers + 2):
-            model = getattr(self, 'model' + str(n))
-            res.append(model(res[-1]))
-        return res[1:]
-
-    def forward(self, x):
-        act = self.get_all_activations(x)
-        return act[-1], act[:-1]
-
-
-class NLayerDiscriminatorAsGen(NLayerDiscriminator):
-    def forward(self, x):
-        return super().forward(x)[0]

extensions-builtin/forge_preprocessor_inpaint/annotator/lama/saicinpainting/training/modules/spatial_transform.py

@@ -1,49 +0,0 @@
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-from kornia.geometry.transform import rotate
-
-
-class LearnableSpatialTransformWrapper(nn.Module):
-    def __init__(self, impl, pad_coef=0.5, angle_init_range=80, train_angle=True):
-        super().__init__()
-        self.impl = impl
-        self.angle = torch.rand(1) * angle_init_range
-        if train_angle:
-            self.angle = nn.Parameter(self.angle, requires_grad=True)
-        self.pad_coef = pad_coef
-
-    def forward(self, x):
-        if torch.is_tensor(x):
-            return self.inverse_transform(self.impl(self.transform(x)), x)
-        elif isinstance(x, tuple):
-            x_trans = tuple(self.transform(elem) for elem in x)
-            y_trans = self.impl(x_trans)
-            return tuple(self.inverse_transform(elem, orig_x) for elem, orig_x in zip(y_trans, x))
-        else:
-            raise ValueError(f'Unexpected input type {type(x)}')
-
-    def transform(self, x):
-        height, width = x.shape[2:]
-        pad_h, pad_w = int(height * self.pad_coef), int(width * self.pad_coef)
-        x_padded = F.pad(x, [pad_w, pad_w, pad_h, pad_h], mode='reflect')
-        x_padded_rotated = rotate(x_padded, angle=self.angle.to(x_padded))
-        return x_padded_rotated
-
-    def inverse_transform(self, y_padded_rotated, orig_x):
-        height, width = orig_x.shape[2:]
-        pad_h, pad_w = int(height * self.pad_coef), int(width * self.pad_coef)
-
-        y_padded = rotate(y_padded_rotated, angle=-self.angle.to(y_padded_rotated))
-        y_height, y_width = y_padded.shape[2:]
-        y = y_padded[:, :, pad_h : y_height - pad_h, pad_w : y_width - pad_w]
-        return y
-
-
-if __name__ == '__main__':
-    layer = LearnableSpatialTransformWrapper(nn.Identity())
-    x = torch.arange(2* 3 * 15 * 15).view(2, 3, 15, 15).float()
-    y = layer(x)
-    assert x.shape == y.shape
-    assert torch.allclose(x[:, :, 1:, 1:][:, :, :-1, :-1], y[:, :, 1:, 1:][:, :, :-1, :-1])
-    print('all ok')

extensions-builtin/forge_preprocessor_inpaint/annotator/lama/saicinpainting/training/modules/squeeze_excitation.py

@@ -1,20 +0,0 @@
-import torch.nn as nn
-
-
-class SELayer(nn.Module):
-    def __init__(self, channel, reduction=16):
-        super(SELayer, self).__init__()
-        self.avg_pool = nn.AdaptiveAvgPool2d(1)
-        self.fc = nn.Sequential(
-            nn.Linear(channel, channel // reduction, bias=False),
-            nn.ReLU(inplace=True),
-            nn.Linear(channel // reduction, channel, bias=False),
-            nn.Sigmoid()
-        )
-
-    def forward(self, x):
-        b, c, _, _ = x.size()
-        y = self.avg_pool(x).view(b, c)
-        y = self.fc(y).view(b, c, 1, 1)
-        res = x * y.expand_as(x)
-        return res

extensions-builtin/forge_preprocessor_inpaint/annotator/lama/saicinpainting/training/trainers/__init__.py

@@ -1,29 +0,0 @@
-import logging
-import torch
-from annotator.lama.saicinpainting.training.trainers.default import DefaultInpaintingTrainingModule
-
-
-def get_training_model_class(kind):
-    if kind == 'default':
-        return DefaultInpaintingTrainingModule
-
-    raise ValueError(f'Unknown trainer module {kind}')
-
-
-def make_training_model(config):
-    kind = config.training_model.kind
-    kwargs = dict(config.training_model)
-    kwargs.pop('kind')
-    kwargs['use_ddp'] = config.trainer.kwargs.get('accelerator', None) == 'ddp'
-
-    logging.info(f'Make training model {kind}')
-
-    cls = get_training_model_class(kind)
-    return cls(config, **kwargs)
-
-
-def load_checkpoint(train_config, path, map_location='cuda', strict=True):
-    model = make_training_model(train_config).generator
-    state = torch.load(path, map_location=map_location)
-    model.load_state_dict(state, strict=strict)
-    return model

extensions-builtin/forge_preprocessor_inpaint/annotator/lama/saicinpainting/training/trainers/base.py

@@ -1,293 +0,0 @@
-import copy
-import logging
-from typing import Dict, Tuple
-
-import pandas as pd
-import pytorch_lightning as ptl
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-# from torch.utils.data import DistributedSampler
-
-# from annotator.lama.saicinpainting.evaluation import make_evaluator
-# from annotator.lama.saicinpainting.training.data.datasets import make_default_train_dataloader, make_default_val_dataloader
-# from annotator.lama.saicinpainting.training.losses.adversarial import make_discrim_loss
-# from annotator.lama.saicinpainting.training.losses.perceptual import PerceptualLoss, ResNetPL
-from annotator.lama.saicinpainting.training.modules import make_generator  #, make_discriminator
-# from annotator.lama.saicinpainting.training.visualizers import make_visualizer
-from annotator.lama.saicinpainting.utils import add_prefix_to_keys, average_dicts, set_requires_grad, flatten_dict, \
-    get_has_ddp_rank
-
-LOGGER = logging.getLogger(__name__)
-
-
-def make_optimizer(parameters, kind='adamw', **kwargs):
-    if kind == 'adam':
-        optimizer_class = torch.optim.Adam
-    elif kind == 'adamw':
-        optimizer_class = torch.optim.AdamW
-    else:
-        raise ValueError(f'Unknown optimizer kind {kind}')
-    return optimizer_class(parameters, **kwargs)
-
-
-def update_running_average(result: nn.Module, new_iterate_model: nn.Module, decay=0.999):
-    with torch.no_grad():
-        res_params = dict(result.named_parameters())
-        new_params = dict(new_iterate_model.named_parameters())
-
-        for k in res_params.keys():
-            res_params[k].data.mul_(decay).add_(new_params[k].data, alpha=1 - decay)
-
-
-def make_multiscale_noise(base_tensor, scales=6, scale_mode='bilinear'):
-    batch_size, _, height, width = base_tensor.shape
-    cur_height, cur_width = height, width
-    result = []
-    align_corners = False if scale_mode in ('bilinear', 'bicubic') else None
-    for _ in range(scales):
-        cur_sample = torch.randn(batch_size, 1, cur_height, cur_width, device=base_tensor.device)
-        cur_sample_scaled = F.interpolate(cur_sample, size=(height, width), mode=scale_mode, align_corners=align_corners)
-        result.append(cur_sample_scaled)
-        cur_height //= 2
-        cur_width //= 2
-    return torch.cat(result, dim=1)
-
-
-class BaseInpaintingTrainingModule(ptl.LightningModule):
-    def __init__(self, config, use_ddp, *args,  predict_only=False, visualize_each_iters=100,
-                 average_generator=False, generator_avg_beta=0.999, average_generator_start_step=30000,
-                 average_generator_period=10, store_discr_outputs_for_vis=False,
-                 **kwargs):
-        super().__init__(*args, **kwargs)
-        LOGGER.info('BaseInpaintingTrainingModule init called')
-
-        self.config = config
-
-        self.generator = make_generator(config, **self.config.generator)
-        self.use_ddp = use_ddp
-
-        if not get_has_ddp_rank():
-            LOGGER.info(f'Generator\n{self.generator}')
-
-        # if not predict_only:
-        #     self.save_hyperparameters(self.config)
-        #     self.discriminator = make_discriminator(**self.config.discriminator)
-        #     self.adversarial_loss = make_discrim_loss(**self.config.losses.adversarial)
-        #     self.visualizer = make_visualizer(**self.config.visualizer)
-        #     self.val_evaluator = make_evaluator(**self.config.evaluator)
-        #     self.test_evaluator = make_evaluator(**self.config.evaluator)
-        #
-        #     if not get_has_ddp_rank():
-        #         LOGGER.info(f'Discriminator\n{self.discriminator}')
-        #
-        #     extra_val = self.config.data.get('extra_val', ())
-        #     if extra_val:
-        #         self.extra_val_titles = list(extra_val)
-        #         self.extra_evaluators = nn.ModuleDict({k: make_evaluator(**self.config.evaluator)
-        #                                                for k in extra_val})
-        #     else:
-        #         self.extra_evaluators = {}
-        #
-        #     self.average_generator = average_generator
-        #     self.generator_avg_beta = generator_avg_beta
-        #     self.average_generator_start_step = average_generator_start_step
-        #     self.average_generator_period = average_generator_period
-        #     self.generator_average = None
-        #     self.last_generator_averaging_step = -1
-        #     self.store_discr_outputs_for_vis = store_discr_outputs_for_vis
-        #
-        #     if self.config.losses.get("l1", {"weight_known": 0})['weight_known'] > 0:
-        #         self.loss_l1 = nn.L1Loss(reduction='none')
-        #
-        #     if self.config.losses.get("mse", {"weight": 0})['weight'] > 0:
-        #         self.loss_mse = nn.MSELoss(reduction='none')
-        #
-        #     if self.config.losses.perceptual.weight > 0:
-        #         self.loss_pl = PerceptualLoss()
-        #
-        #     # if self.config.losses.get("resnet_pl", {"weight": 0})['weight'] > 0:
-        #     #     self.loss_resnet_pl = ResNetPL(**self.config.losses.resnet_pl)
-        #     # else:
-        #     #     self.loss_resnet_pl = None
-        #
-        #     self.loss_resnet_pl = None
-
-        self.visualize_each_iters = visualize_each_iters
-        LOGGER.info('BaseInpaintingTrainingModule init done')
-
-    def configure_optimizers(self):
-        discriminator_params = list(self.discriminator.parameters())
-        return [
-            dict(optimizer=make_optimizer(self.generator.parameters(), **self.config.optimizers.generator)),
-            dict(optimizer=make_optimizer(discriminator_params, **self.config.optimizers.discriminator)),
-        ]
-
-    def train_dataloader(self):
-        kwargs = dict(self.config.data.train)
-        if self.use_ddp:
-            kwargs['ddp_kwargs'] = dict(num_replicas=self.trainer.num_nodes * self.trainer.num_processes,
-                                        rank=self.trainer.global_rank,
-                                        shuffle=True)
-        dataloader = make_default_train_dataloader(**self.config.data.train)
-        return dataloader
-
-    def val_dataloader(self):
-        res = [make_default_val_dataloader(**self.config.data.val)]
-
-        if self.config.data.visual_test is not None:
-            res = res + [make_default_val_dataloader(**self.config.data.visual_test)]
-        else:
-            res = res + res
-
-        extra_val = self.config.data.get('extra_val', ())
-        if extra_val:
-            res += [make_default_val_dataloader(**extra_val[k]) for k in self.extra_val_titles]
-
-        return res
-
-    def training_step(self, batch, batch_idx, optimizer_idx=None):
-        self._is_training_step = True
-        return self._do_step(batch, batch_idx, mode='train', optimizer_idx=optimizer_idx)
-
-    def validation_step(self, batch, batch_idx, dataloader_idx):
-        extra_val_key = None
-        if dataloader_idx == 0:
-            mode = 'val'
-        elif dataloader_idx == 1:
-            mode = 'test'
-        else:
-            mode = 'extra_val'
-            extra_val_key = self.extra_val_titles[dataloader_idx - 2]
-        self._is_training_step = False
-        return self._do_step(batch, batch_idx, mode=mode, extra_val_key=extra_val_key)
-
-    def training_step_end(self, batch_parts_outputs):
-        if self.training and self.average_generator \
-                and self.global_step >= self.average_generator_start_step \
-                and self.global_step >= self.last_generator_averaging_step + self.average_generator_period:
-            if self.generator_average is None:
-                self.generator_average = copy.deepcopy(self.generator)
-            else:
-                update_running_average(self.generator_average, self.generator, decay=self.generator_avg_beta)
-            self.last_generator_averaging_step = self.global_step
-
-        full_loss = (batch_parts_outputs['loss'].mean()
-                     if torch.is_tensor(batch_parts_outputs['loss'])  # loss is not tensor when no discriminator used
-                     else torch.tensor(batch_parts_outputs['loss']).float().requires_grad_(True))
-        log_info = {k: v.mean() for k, v in batch_parts_outputs['log_info'].items()}
-        self.log_dict(log_info, on_step=True, on_epoch=False)
-        return full_loss
-
-    def validation_epoch_end(self, outputs):
-        outputs = [step_out for out_group in outputs for step_out in out_group]
-        averaged_logs = average_dicts(step_out['log_info'] for step_out in outputs)
-        self.log_dict({k: v.mean() for k, v in averaged_logs.items()})
-
-        pd.set_option('display.max_columns', 500)
-        pd.set_option('display.width', 1000)
-
-        # standard validation
-        val_evaluator_states = [s['val_evaluator_state'] for s in outputs if 'val_evaluator_state' in s]
-        val_evaluator_res = self.val_evaluator.evaluation_end(states=val_evaluator_states)
-        val_evaluator_res_df = pd.DataFrame(val_evaluator_res).stack(1).unstack(0)
-        val_evaluator_res_df.dropna(axis=1, how='all', inplace=True)
-        LOGGER.info(f'Validation metrics after epoch #{self.current_epoch}, '
-                    f'total {self.global_step} iterations:\n{val_evaluator_res_df}')
-
-        for k, v in flatten_dict(val_evaluator_res).items():
-            self.log(f'val_{k}', v)
-
-        # standard visual test
-        test_evaluator_states = [s['test_evaluator_state'] for s in outputs
-                                 if 'test_evaluator_state' in s]
-        test_evaluator_res = self.test_evaluator.evaluation_end(states=test_evaluator_states)
-        test_evaluator_res_df = pd.DataFrame(test_evaluator_res).stack(1).unstack(0)
-        test_evaluator_res_df.dropna(axis=1, how='all', inplace=True)
-        LOGGER.info(f'Test metrics after epoch #{self.current_epoch}, '
-                    f'total {self.global_step} iterations:\n{test_evaluator_res_df}')
-
-        for k, v in flatten_dict(test_evaluator_res).items():
-            self.log(f'test_{k}', v)
-
-        # extra validations
-        if self.extra_evaluators:
-            for cur_eval_title, cur_evaluator in self.extra_evaluators.items():
-                cur_state_key = f'extra_val_{cur_eval_title}_evaluator_state'
-                cur_states = [s[cur_state_key] for s in outputs if cur_state_key in s]
-                cur_evaluator_res = cur_evaluator.evaluation_end(states=cur_states)
-                cur_evaluator_res_df = pd.DataFrame(cur_evaluator_res).stack(1).unstack(0)
-                cur_evaluator_res_df.dropna(axis=1, how='all', inplace=True)
-                LOGGER.info(f'Extra val {cur_eval_title} metrics after epoch #{self.current_epoch}, '
-                            f'total {self.global_step} iterations:\n{cur_evaluator_res_df}')
-                for k, v in flatten_dict(cur_evaluator_res).items():
-                    self.log(f'extra_val_{cur_eval_title}_{k}', v)
-
-    def _do_step(self, batch, batch_idx, mode='train', optimizer_idx=None, extra_val_key=None):
-        if optimizer_idx == 0:  # step for generator
-            set_requires_grad(self.generator, True)
-            set_requires_grad(self.discriminator, False)
-        elif optimizer_idx == 1:  # step for discriminator
-            set_requires_grad(self.generator, False)
-            set_requires_grad(self.discriminator, True)
-
-        batch = self(batch)
-
-        total_loss = 0
-        metrics = {}
-
-        if optimizer_idx is None or optimizer_idx == 0:  # step for generator
-            total_loss, metrics = self.generator_loss(batch)
-
-        elif optimizer_idx is None or optimizer_idx == 1:  # step for discriminator
-            if self.config.losses.adversarial.weight > 0:
-                total_loss, metrics = self.discriminator_loss(batch)
-
-        if self.get_ddp_rank() in (None, 0) and (batch_idx % self.visualize_each_iters == 0 or mode == 'test'):
-            if self.config.losses.adversarial.weight > 0:
-                if self.store_discr_outputs_for_vis:
-                    with torch.no_grad():
-                        self.store_discr_outputs(batch)
-            vis_suffix = f'_{mode}'
-            if mode == 'extra_val':
-                vis_suffix += f'_{extra_val_key}'
-            self.visualizer(self.current_epoch, batch_idx, batch, suffix=vis_suffix)
-
-        metrics_prefix = f'{mode}_'
-        if mode == 'extra_val':
-            metrics_prefix += f'{extra_val_key}_'
-        result = dict(loss=total_loss, log_info=add_prefix_to_keys(metrics, metrics_prefix))
-        if mode == 'val':
-            result['val_evaluator_state'] = self.val_evaluator.process_batch(batch)
-        elif mode == 'test':
-            result['test_evaluator_state'] = self.test_evaluator.process_batch(batch)
-        elif mode == 'extra_val':
-            result[f'extra_val_{extra_val_key}_evaluator_state'] = self.extra_evaluators[extra_val_key].process_batch(batch)
-
-        return result
-
-    def get_current_generator(self, no_average=False):
-        if not no_average and not self.training and self.average_generator and self.generator_average is not None:
-            return self.generator_average
-        return self.generator
-
-    def forward(self, batch: Dict[str, torch.Tensor]) -> Dict[str, torch.Tensor]:
-        """Pass data through generator and obtain at leas 'predicted_image' and 'inpainted' keys"""
-        raise NotImplementedError()
-
-    def generator_loss(self, batch) -> Tuple[torch.Tensor, Dict[str, torch.Tensor]]:
-        raise NotImplementedError()
-
-    def discriminator_loss(self, batch) -> Tuple[torch.Tensor, Dict[str, torch.Tensor]]:
-        raise NotImplementedError()
-
-    def store_discr_outputs(self, batch):
-        out_size = batch['image'].shape[2:]
-        discr_real_out, _ = self.discriminator(batch['image'])
-        discr_fake_out, _ = self.discriminator(batch['predicted_image'])
-        batch['discr_output_real'] = F.interpolate(discr_real_out, size=out_size, mode='nearest')
-        batch['discr_output_fake'] = F.interpolate(discr_fake_out, size=out_size, mode='nearest')
-        batch['discr_output_diff'] = batch['discr_output_real'] - batch['discr_output_fake']
-
-    def get_ddp_rank(self):
-        return self.trainer.global_rank if (self.trainer.num_nodes * self.trainer.num_processes) > 1 else None

extensions-builtin/forge_preprocessor_inpaint/annotator/lama/saicinpainting/training/trainers/default.py

@@ -1,175 +0,0 @@
-import logging
-
-import torch
-import torch.nn.functional as F
-from omegaconf import OmegaConf
-
-# from annotator.lama.saicinpainting.training.data.datasets import make_constant_area_crop_params
-from annotator.lama.saicinpainting.training.losses.distance_weighting import make_mask_distance_weighter
-from annotator.lama.saicinpainting.training.losses.feature_matching import feature_matching_loss, masked_l1_loss
-# from annotator.lama.saicinpainting.training.modules.fake_fakes import FakeFakesGenerator
-from annotator.lama.saicinpainting.training.trainers.base import BaseInpaintingTrainingModule, make_multiscale_noise
-from annotator.lama.saicinpainting.utils import add_prefix_to_keys, get_ramp
-
-LOGGER = logging.getLogger(__name__)
-
-
-def make_constant_area_crop_batch(batch, **kwargs):
-    crop_y, crop_x, crop_height, crop_width = make_constant_area_crop_params(img_height=batch['image'].shape[2],
-                                                                             img_width=batch['image'].shape[3],
-                                                                             **kwargs)
-    batch['image'] = batch['image'][:, :, crop_y : crop_y + crop_height, crop_x : crop_x + crop_width]
-    batch['mask'] = batch['mask'][:, :, crop_y: crop_y + crop_height, crop_x: crop_x + crop_width]
-    return batch
-
-
-class DefaultInpaintingTrainingModule(BaseInpaintingTrainingModule):
-    def __init__(self, *args, concat_mask=True, rescale_scheduler_kwargs=None, image_to_discriminator='predicted_image',
-                 add_noise_kwargs=None, noise_fill_hole=False, const_area_crop_kwargs=None,
-                 distance_weighter_kwargs=None, distance_weighted_mask_for_discr=False,
-                 fake_fakes_proba=0, fake_fakes_generator_kwargs=None,
-                 **kwargs):
-        super().__init__(*args, **kwargs)
-        self.concat_mask = concat_mask
-        self.rescale_size_getter = get_ramp(**rescale_scheduler_kwargs) if rescale_scheduler_kwargs is not None else None
-        self.image_to_discriminator = image_to_discriminator
-        self.add_noise_kwargs = add_noise_kwargs
-        self.noise_fill_hole = noise_fill_hole
-        self.const_area_crop_kwargs = const_area_crop_kwargs
-        self.refine_mask_for_losses = make_mask_distance_weighter(**distance_weighter_kwargs) \
-            if distance_weighter_kwargs is not None else None
-        self.distance_weighted_mask_for_discr = distance_weighted_mask_for_discr
-
-        self.fake_fakes_proba = fake_fakes_proba
-        if self.fake_fakes_proba > 1e-3:
-            self.fake_fakes_gen = FakeFakesGenerator(**(fake_fakes_generator_kwargs or {}))
-
-    def forward(self, batch):
-        if self.training and self.rescale_size_getter is not None:
-            cur_size = self.rescale_size_getter(self.global_step)
-            batch['image'] = F.interpolate(batch['image'], size=cur_size, mode='bilinear', align_corners=False)
-            batch['mask'] = F.interpolate(batch['mask'], size=cur_size, mode='nearest')
-
-        if self.training and self.const_area_crop_kwargs is not None:
-            batch = make_constant_area_crop_batch(batch, **self.const_area_crop_kwargs)
-
-        img = batch['image']
-        mask = batch['mask']
-
-        masked_img = img * (1 - mask)
-
-        if self.add_noise_kwargs is not None:
-            noise = make_multiscale_noise(masked_img, **self.add_noise_kwargs)
-            if self.noise_fill_hole:
-                masked_img = masked_img + mask * noise[:, :masked_img.shape[1]]
-            masked_img = torch.cat([masked_img, noise], dim=1)
-
-        if self.concat_mask:
-            masked_img = torch.cat([masked_img, mask], dim=1)
-
-        batch['predicted_image'] = self.generator(masked_img)
-        batch['inpainted'] = mask * batch['predicted_image'] + (1 - mask) * batch['image']
-
-        if self.fake_fakes_proba > 1e-3:
-            if self.training and torch.rand(1).item() < self.fake_fakes_proba:
-                batch['fake_fakes'], batch['fake_fakes_masks'] = self.fake_fakes_gen(img, mask)
-                batch['use_fake_fakes'] = True
-            else:
-                batch['fake_fakes'] = torch.zeros_like(img)
-                batch['fake_fakes_masks'] = torch.zeros_like(mask)
-                batch['use_fake_fakes'] = False
-
-        batch['mask_for_losses'] = self.refine_mask_for_losses(img, batch['predicted_image'], mask) \
-            if self.refine_mask_for_losses is not None and self.training \
-            else mask
-
-        return batch
-
-    def generator_loss(self, batch):
-        img = batch['image']
-        predicted_img = batch[self.image_to_discriminator]
-        original_mask = batch['mask']
-        supervised_mask = batch['mask_for_losses']
-
-        # L1
-        l1_value = masked_l1_loss(predicted_img, img, supervised_mask,
-                                  self.config.losses.l1.weight_known,
-                                  self.config.losses.l1.weight_missing)
-
-        total_loss = l1_value
-        metrics = dict(gen_l1=l1_value)
-
-        # vgg-based perceptual loss
-        if self.config.losses.perceptual.weight > 0:
-            pl_value = self.loss_pl(predicted_img, img, mask=supervised_mask).sum() * self.config.losses.perceptual.weight
-            total_loss = total_loss + pl_value
-            metrics['gen_pl'] = pl_value
-
-        # discriminator
-        # adversarial_loss calls backward by itself
-        mask_for_discr = supervised_mask if self.distance_weighted_mask_for_discr else original_mask
-        self.adversarial_loss.pre_generator_step(real_batch=img, fake_batch=predicted_img,
-                                                 generator=self.generator, discriminator=self.discriminator)
-        discr_real_pred, discr_real_features = self.discriminator(img)
-        discr_fake_pred, discr_fake_features = self.discriminator(predicted_img)
-        adv_gen_loss, adv_metrics = self.adversarial_loss.generator_loss(real_batch=img,
-                                                                         fake_batch=predicted_img,
-                                                                         discr_real_pred=discr_real_pred,
-                                                                         discr_fake_pred=discr_fake_pred,
-                                                                         mask=mask_for_discr)
-        total_loss = total_loss + adv_gen_loss
-        metrics['gen_adv'] = adv_gen_loss
-        metrics.update(add_prefix_to_keys(adv_metrics, 'adv_'))
-
-        # feature matching
-        if self.config.losses.feature_matching.weight > 0:
-            need_mask_in_fm = OmegaConf.to_container(self.config.losses.feature_matching).get('pass_mask', False)
-            mask_for_fm = supervised_mask if need_mask_in_fm else None
-            fm_value = feature_matching_loss(discr_fake_features, discr_real_features,
-                                             mask=mask_for_fm) * self.config.losses.feature_matching.weight
-            total_loss = total_loss + fm_value
-            metrics['gen_fm'] = fm_value
-
-        if self.loss_resnet_pl is not None:
-            resnet_pl_value = self.loss_resnet_pl(predicted_img, img)
-            total_loss = total_loss + resnet_pl_value
-            metrics['gen_resnet_pl'] = resnet_pl_value
-
-        return total_loss, metrics
-
-    def discriminator_loss(self, batch):
-        total_loss = 0
-        metrics = {}
-
-        predicted_img = batch[self.image_to_discriminator].detach()
-        self.adversarial_loss.pre_discriminator_step(real_batch=batch['image'], fake_batch=predicted_img,
-                                                     generator=self.generator, discriminator=self.discriminator)
-        discr_real_pred, discr_real_features = self.discriminator(batch['image'])
-        discr_fake_pred, discr_fake_features = self.discriminator(predicted_img)
-        adv_discr_loss, adv_metrics = self.adversarial_loss.discriminator_loss(real_batch=batch['image'],
-                                                                               fake_batch=predicted_img,
-                                                                               discr_real_pred=discr_real_pred,
-                                                                               discr_fake_pred=discr_fake_pred,
-                                                                               mask=batch['mask'])
-        total_loss = total_loss + adv_discr_loss
-        metrics['discr_adv'] = adv_discr_loss
-        metrics.update(add_prefix_to_keys(adv_metrics, 'adv_'))
-
-
-        if batch.get('use_fake_fakes', False):
-            fake_fakes = batch['fake_fakes']
-            self.adversarial_loss.pre_discriminator_step(real_batch=batch['image'], fake_batch=fake_fakes,
-                                                         generator=self.generator, discriminator=self.discriminator)
-            discr_fake_fakes_pred, _ = self.discriminator(fake_fakes)
-            fake_fakes_adv_discr_loss, fake_fakes_adv_metrics = self.adversarial_loss.discriminator_loss(
-                real_batch=batch['image'],
-                fake_batch=fake_fakes,
-                discr_real_pred=discr_real_pred,
-                discr_fake_pred=discr_fake_fakes_pred,
-                mask=batch['mask']
-            )
-            total_loss = total_loss + fake_fakes_adv_discr_loss
-            metrics['discr_adv_fake_fakes'] = fake_fakes_adv_discr_loss
-            metrics.update(add_prefix_to_keys(fake_fakes_adv_metrics, 'adv_'))
-
-        return total_loss, metrics

extensions-builtin/forge_preprocessor_inpaint/annotator/lama/saicinpainting/training/visualizers/__init__.py

@@ -1,15 +0,0 @@
-import logging
-
-from annotator.lama.saicinpainting.training.visualizers.directory import DirectoryVisualizer
-from annotator.lama.saicinpainting.training.visualizers.noop import NoopVisualizer
-
-
-def make_visualizer(kind, **kwargs):
-    logging.info(f'Make visualizer {kind}')
-
-    if kind == 'directory':
-        return DirectoryVisualizer(**kwargs)
-    if kind == 'noop':
-        return NoopVisualizer()
-
-    raise ValueError(f'Unknown visualizer kind {kind}')

extensions-builtin/forge_preprocessor_inpaint/annotator/lama/saicinpainting/training/visualizers/base.py

@@ -1,73 +0,0 @@
-import abc
-from typing import Dict, List
-
-import numpy as np
-import torch
-from skimage import color
-from skimage.segmentation import mark_boundaries
-
-from . import colors
-
-COLORS, _ = colors.generate_colors(151) # 151 - max classes for semantic segmentation
-
-
-class BaseVisualizer:
-    @abc.abstractmethod
-    def __call__(self, epoch_i, batch_i, batch, suffix='', rank=None):
-        """
-        Take a batch, make an image from it and visualize
-        """
-        raise NotImplementedError()
-
-
-def visualize_mask_and_images(images_dict: Dict[str, np.ndarray], keys: List[str],
-                              last_without_mask=True, rescale_keys=None, mask_only_first=None,
-                              black_mask=False) -> np.ndarray:
-    mask = images_dict['mask'] > 0.5
-    result = []
-    for i, k in enumerate(keys):
-        img = images_dict[k]
-        img = np.transpose(img, (1, 2, 0))
-
-        if rescale_keys is not None and k in rescale_keys:
-            img = img - img.min()
-            img /= img.max() + 1e-5
-        if len(img.shape) == 2:
-            img = np.expand_dims(img, 2)
-
-        if img.shape[2] == 1:
-            img = np.repeat(img, 3, axis=2)
-        elif (img.shape[2] > 3):
-            img_classes = img.argmax(2)
-            img = color.label2rgb(img_classes, colors=COLORS)
-
-        if mask_only_first:
-            need_mark_boundaries = i == 0
-        else:
-            need_mark_boundaries = i < len(keys) - 1 or not last_without_mask
-
-        if need_mark_boundaries:
-            if black_mask:
-                img = img * (1 - mask[0][..., None])
-            img = mark_boundaries(img,
-                                  mask[0],
-                                  color=(1., 0., 0.),
-                                  outline_color=(1., 1., 1.),
-                                  mode='thick')
-        result.append(img)
-    return np.concatenate(result, axis=1)
-
-
-def visualize_mask_and_images_batch(batch: Dict[str, torch.Tensor], keys: List[str], max_items=10,
-                                    last_without_mask=True, rescale_keys=None) -> np.ndarray:
-    batch = {k: tens.detach().cpu().numpy() for k, tens in batch.items()
-             if k in keys or k == 'mask'}
-
-    batch_size = next(iter(batch.values())).shape[0]
-    items_to_vis = min(batch_size, max_items)
-    result = []
-    for i in range(items_to_vis):
-        cur_dct = {k: tens[i] for k, tens in batch.items()}
-        result.append(visualize_mask_and_images(cur_dct, keys, last_without_mask=last_without_mask,
-                                                rescale_keys=rescale_keys))
-    return np.concatenate(result, axis=0)

extensions-builtin/forge_preprocessor_inpaint/annotator/lama/saicinpainting/training/visualizers/colors.py

@@ -1,76 +0,0 @@
-import random
-import colorsys
-
-import numpy as np
-import matplotlib
-matplotlib.use('agg')
-import matplotlib.pyplot as plt 
-from matplotlib.colors import LinearSegmentedColormap
-
-
-def generate_colors(nlabels, type='bright', first_color_black=False, last_color_black=True, verbose=False):
-    # https://stackoverflow.com/questions/14720331/how-to-generate-random-colors-in-matplotlib
-    """
-    Creates a random colormap to be used together with matplotlib. Useful for segmentation tasks
-    :param nlabels: Number of labels (size of colormap)
-    :param type: 'bright' for strong colors, 'soft' for pastel colors
-    :param first_color_black: Option to use first color as black, True or False
-    :param last_color_black: Option to use last color as black, True or False
-    :param verbose: Prints the number of labels and shows the colormap. True or False
-    :return: colormap for matplotlib
-    """
-    if type not in ('bright', 'soft'):
-        print ('Please choose "bright" or "soft" for type')
-        return
-
-    if verbose:
-        print('Number of labels: ' + str(nlabels))
-
-    # Generate color map for bright colors, based on hsv
-    if type == 'bright':
-        randHSVcolors = [(np.random.uniform(low=0.0, high=1),
-                          np.random.uniform(low=0.2, high=1),
-                          np.random.uniform(low=0.9, high=1)) for i in range(nlabels)]
-
-        # Convert HSV list to RGB
-        randRGBcolors = []
-        for HSVcolor in randHSVcolors:
-            randRGBcolors.append(colorsys.hsv_to_rgb(HSVcolor[0], HSVcolor[1], HSVcolor[2]))
-
-        if first_color_black:
-            randRGBcolors[0] = [0, 0, 0]
-
-        if last_color_black:
-            randRGBcolors[-1] = [0, 0, 0]
-
-        random_colormap = LinearSegmentedColormap.from_list('new_map', randRGBcolors, N=nlabels)
-
-    # Generate soft pastel colors, by limiting the RGB spectrum
-    if type == 'soft':
-        low = 0.6
-        high = 0.95
-        randRGBcolors = [(np.random.uniform(low=low, high=high),
-                          np.random.uniform(low=low, high=high),
-                          np.random.uniform(low=low, high=high)) for i in range(nlabels)]
-
-        if first_color_black:
-            randRGBcolors[0] = [0, 0, 0]
-
-        if last_color_black:
-            randRGBcolors[-1] = [0, 0, 0]
-        random_colormap = LinearSegmentedColormap.from_list('new_map', randRGBcolors, N=nlabels)
-
-    # Display colorbar
-    if verbose:
-        from matplotlib import colors, colorbar
-        from matplotlib import pyplot as plt
-        fig, ax = plt.subplots(1, 1, figsize=(15, 0.5))
-
-        bounds = np.linspace(0, nlabels, nlabels + 1)
-        norm = colors.BoundaryNorm(bounds, nlabels)
-
-        cb = colorbar.ColorbarBase(ax, cmap=random_colormap, norm=norm, spacing='proportional', ticks=None,
-                                   boundaries=bounds, format='%1i', orientation=u'horizontal')
-
-    return randRGBcolors, random_colormap
-

extensions-builtin/forge_preprocessor_inpaint/annotator/lama/saicinpainting/training/visualizers/directory.py

@@ -1,36 +0,0 @@
-import os
-
-import cv2
-import numpy as np
-
-from annotator.lama.saicinpainting.training.visualizers.base import BaseVisualizer, visualize_mask_and_images_batch
-from annotator.lama.saicinpainting.utils import check_and_warn_input_range
-
-
-class DirectoryVisualizer(BaseVisualizer):
-    DEFAULT_KEY_ORDER = 'image predicted_image inpainted'.split(' ')
-
-    def __init__(self, outdir, key_order=DEFAULT_KEY_ORDER, max_items_in_batch=10,
-                 last_without_mask=True, rescale_keys=None):
-        self.outdir = outdir
-        os.makedirs(self.outdir, exist_ok=True)
-        self.key_order = key_order
-        self.max_items_in_batch = max_items_in_batch
-        self.last_without_mask = last_without_mask
-        self.rescale_keys = rescale_keys
-
-    def __call__(self, epoch_i, batch_i, batch, suffix='', rank=None):
-        check_and_warn_input_range(batch['image'], 0, 1, 'DirectoryVisualizer target image')
-        vis_img = visualize_mask_and_images_batch(batch, self.key_order, max_items=self.max_items_in_batch,
-                                                  last_without_mask=self.last_without_mask,
-                                                  rescale_keys=self.rescale_keys)
-
-        vis_img = np.clip(vis_img * 255, 0, 255).astype('uint8')
-
-        curoutdir = os.path.join(self.outdir, f'epoch{epoch_i:04d}{suffix}')
-        os.makedirs(curoutdir, exist_ok=True)
-        rank_suffix = f'_r{rank}' if rank is not None else ''
-        out_fname = os.path.join(curoutdir, f'batch{batch_i:07d}{rank_suffix}.jpg')
-
-        vis_img = cv2.cvtColor(vis_img, cv2.COLOR_RGB2BGR)
-        cv2.imwrite(out_fname, vis_img)

extensions-builtin/forge_preprocessor_inpaint/annotator/lama/saicinpainting/training/visualizers/noop.py

@@ -1,9 +0,0 @@
-from annotator.lama.saicinpainting.training.visualizers.base import BaseVisualizer
-
-
-class NoopVisualizer(BaseVisualizer):
-    def __init__(self, *args, **kwargs):
-        pass
-
-    def __call__(self, epoch_i, batch_i, batch, suffix='', rank=None):
-        pass

extensions-builtin/forge_preprocessor_inpaint/annotator/lama/saicinpainting/utils.py

@@ -1,174 +0,0 @@
-import bisect
-import functools
-import logging
-import numbers
-import os
-import signal
-import sys
-import traceback
-import warnings
-
-import torch
-from pytorch_lightning import seed_everything
-
-LOGGER = logging.getLogger(__name__)
-
-
-def check_and_warn_input_range(tensor, min_value, max_value, name):
-    actual_min = tensor.min()
-    actual_max = tensor.max()
-    if actual_min < min_value or actual_max > max_value:
-        warnings.warn(f"{name} must be in {min_value}..{max_value} range, but it ranges {actual_min}..{actual_max}")
-
-
-def sum_dict_with_prefix(target, cur_dict, prefix, default=0):
-    for k, v in cur_dict.items():
-        target_key = prefix + k
-        target[target_key] = target.get(target_key, default) + v
-
-
-def average_dicts(dict_list):
-    result = {}
-    norm = 1e-3
-    for dct in dict_list:
-        sum_dict_with_prefix(result, dct, '')
-        norm += 1
-    for k in list(result):
-        result[k] /= norm
-    return result
-
-
-def add_prefix_to_keys(dct, prefix):
-    return {prefix + k: v for k, v in dct.items()}
-
-
-def set_requires_grad(module, value):
-    for param in module.parameters():
-        param.requires_grad = value
-
-
-def flatten_dict(dct):
-    result = {}
-    for k, v in dct.items():
-        if isinstance(k, tuple):
-            k = '_'.join(k)
-        if isinstance(v, dict):
-            for sub_k, sub_v in flatten_dict(v).items():
-                result[f'{k}_{sub_k}'] = sub_v
-        else:
-            result[k] = v
-    return result
-
-
-class LinearRamp:
-    def __init__(self, start_value=0, end_value=1, start_iter=-1, end_iter=0):
-        self.start_value = start_value
-        self.end_value = end_value
-        self.start_iter = start_iter
-        self.end_iter = end_iter
-
-    def __call__(self, i):
-        if i < self.start_iter:
-            return self.start_value
-        if i >= self.end_iter:
-            return self.end_value
-        part = (i - self.start_iter) / (self.end_iter - self.start_iter)
-        return self.start_value * (1 - part) + self.end_value * part
-
-
-class LadderRamp:
-    def __init__(self, start_iters, values):
-        self.start_iters = start_iters
-        self.values = values
-        assert len(values) == len(start_iters) + 1, (len(values), len(start_iters))
-
-    def __call__(self, i):
-        segment_i = bisect.bisect_right(self.start_iters, i)
-        return self.values[segment_i]
-
-
-def get_ramp(kind='ladder', **kwargs):
-    if kind == 'linear':
-        return LinearRamp(**kwargs)
-    if kind == 'ladder':
-        return LadderRamp(**kwargs)
-    raise ValueError(f'Unexpected ramp kind: {kind}')
-
-
-def print_traceback_handler(sig, frame):
-    LOGGER.warning(f'Received signal {sig}')
-    bt = ''.join(traceback.format_stack())
-    LOGGER.warning(f'Requested stack trace:\n{bt}')
-
-
-def register_debug_signal_handlers(sig=None, handler=print_traceback_handler):
-    LOGGER.warning(f'Setting signal {sig} handler {handler}')
-    signal.signal(sig, handler)
-
-
-def handle_deterministic_config(config):
-    seed = dict(config).get('seed', None)
-    if seed is None:
-        return False
-
-    seed_everything(seed)
-    return True
-
-
-def get_shape(t):
-    if torch.is_tensor(t):
-        return tuple(t.shape)
-    elif isinstance(t, dict):
-        return {n: get_shape(q) for n, q in t.items()}
-    elif isinstance(t, (list, tuple)):
-        return [get_shape(q) for q in t]
-    elif isinstance(t, numbers.Number):
-        return type(t)
-    else:
-        raise ValueError('unexpected type {}'.format(type(t)))
-
-
-def get_has_ddp_rank():
-    master_port = os.environ.get('MASTER_PORT', None)
-    node_rank = os.environ.get('NODE_RANK', None)
-    local_rank = os.environ.get('LOCAL_RANK', None)
-    world_size = os.environ.get('WORLD_SIZE', None)
-    has_rank = master_port is not None or node_rank is not None or local_rank is not None or world_size is not None
-    return has_rank
-
-
-def handle_ddp_subprocess():
-    def main_decorator(main_func):
-        @functools.wraps(main_func)
-        def new_main(*args, **kwargs):
-            # Trainer sets MASTER_PORT, NODE_RANK, LOCAL_RANK, WORLD_SIZE
-            parent_cwd = os.environ.get('TRAINING_PARENT_WORK_DIR', None)
-            has_parent = parent_cwd is not None
-            has_rank = get_has_ddp_rank()
-            assert has_parent == has_rank, f'Inconsistent state: has_parent={has_parent}, has_rank={has_rank}'
-
-            if has_parent:
-                # we are in the worker
-                sys.argv.extend([
-                    f'hydra.run.dir={parent_cwd}',
-                    # 'hydra/hydra_logging=disabled',
-                    # 'hydra/job_logging=disabled'
-                ])
-            # do nothing if this is a top-level process
-            # TRAINING_PARENT_WORK_DIR is set in handle_ddp_parent_process after hydra initialization
-
-            main_func(*args, **kwargs)
-        return new_main
-    return main_decorator
-
-
-def handle_ddp_parent_process():
-    parent_cwd = os.environ.get('TRAINING_PARENT_WORK_DIR', None)
-    has_parent = parent_cwd is not None
-    has_rank = get_has_ddp_rank()
-    assert has_parent == has_rank, f'Inconsistent state: has_parent={has_parent}, has_rank={has_rank}'
-
-    if parent_cwd is None:
-        os.environ['TRAINING_PARENT_WORK_DIR'] = os.getcwd()
-
-    return has_parent

extensions-builtin/forge_preprocessor_inpaint/scripts/lama_config.yaml

@@ -1,157 +0,0 @@
-run_title: b18_ffc075_batch8x15
-training_model:
-  kind: default
-  visualize_each_iters: 1000
-  concat_mask: true
-  store_discr_outputs_for_vis: true
-losses:
-  l1:
-    weight_missing: 0
-    weight_known: 10
-  perceptual:
-    weight: 0
-  adversarial:
-    kind: r1
-    weight: 10
-    gp_coef: 0.001
-    mask_as_fake_target: true
-    allow_scale_mask: true
-  feature_matching:
-    weight: 100
-  resnet_pl:
-    weight: 30
-    weights_path: ${env:TORCH_HOME}
-
-optimizers:
-  generator:
-    kind: adam
-    lr: 0.001
-  discriminator:
-    kind: adam
-    lr: 0.0001
-visualizer:
-  key_order:
-  - image
-  - predicted_image
-  - discr_output_fake
-  - discr_output_real
-  - inpainted
-  rescale_keys:
-  - discr_output_fake
-  - discr_output_real
-  kind: directory
-  outdir: /group-volume/User-Driven-Content-Generation/r.suvorov/inpainting/experiments/r.suvorov_2021-04-30_14-41-12_train_simple_pix2pix2_gap_sdpl_novgg_large_b18_ffc075_batch8x15/samples
-location:
-  data_root_dir: /group-volume/User-Driven-Content-Generation/datasets/inpainting_data_root_large
-  out_root_dir: /group-volume/User-Driven-Content-Generation/${env:USER}/inpainting/experiments
-  tb_dir: /group-volume/User-Driven-Content-Generation/${env:USER}/inpainting/tb_logs
-data:
-  batch_size: 15
-  val_batch_size: 2
-  num_workers: 3
-  train:
-    indir: ${location.data_root_dir}/train
-    out_size: 256
-    mask_gen_kwargs:
-      irregular_proba: 1
-      irregular_kwargs:
-        max_angle: 4
-        max_len: 200
-        max_width: 100
-        max_times: 5
-        min_times: 1
-      box_proba: 1
-      box_kwargs:
-        margin: 10
-        bbox_min_size: 30
-        bbox_max_size: 150
-        max_times: 3
-        min_times: 1
-      segm_proba: 0
-      segm_kwargs:
-        confidence_threshold: 0.5
-        max_object_area: 0.5
-        min_mask_area: 0.07
-        downsample_levels: 6
-        num_variants_per_mask: 1
-        rigidness_mode: 1
-        max_foreground_coverage: 0.3
-        max_foreground_intersection: 0.7
-        max_mask_intersection: 0.1
-        max_hidden_area: 0.1
-        max_scale_change: 0.25
-        horizontal_flip: true
-        max_vertical_shift: 0.2
-        position_shuffle: true
-    transform_variant: distortions
-    dataloader_kwargs:
-      batch_size: ${data.batch_size}
-      shuffle: true
-      num_workers: ${data.num_workers}
-  val:
-    indir: ${location.data_root_dir}/val
-    img_suffix: .png
-    dataloader_kwargs:
-      batch_size: ${data.val_batch_size}
-      shuffle: false
-      num_workers: ${data.num_workers}
-  visual_test:
-    indir: ${location.data_root_dir}/korean_test
-    img_suffix: _input.png
-    pad_out_to_modulo: 32
-    dataloader_kwargs:
-      batch_size: 1
-      shuffle: false
-      num_workers: ${data.num_workers}
-generator:
-  kind: ffc_resnet
-  input_nc: 4
-  output_nc: 3
-  ngf: 64
-  n_downsampling: 3
-  n_blocks: 18
-  add_out_act: sigmoid
-  init_conv_kwargs:
-    ratio_gin: 0
-    ratio_gout: 0
-    enable_lfu: false
-  downsample_conv_kwargs:
-    ratio_gin: ${generator.init_conv_kwargs.ratio_gout}
-    ratio_gout: ${generator.downsample_conv_kwargs.ratio_gin}
-    enable_lfu: false
-  resnet_conv_kwargs:
-    ratio_gin: 0.75
-    ratio_gout: ${generator.resnet_conv_kwargs.ratio_gin}
-    enable_lfu: false
-discriminator:
-  kind: pix2pixhd_nlayer
-  input_nc: 3
-  ndf: 64
-  n_layers: 4
-evaluator:
-  kind: default
-  inpainted_key: inpainted
-  integral_kind: ssim_fid100_f1
-trainer:
-  kwargs:
-    gpus: -1
-    accelerator: ddp
-    max_epochs: 200
-    gradient_clip_val: 1
-    log_gpu_memory: None
-    limit_train_batches: 25000
-    val_check_interval: ${trainer.kwargs.limit_train_batches}
-    log_every_n_steps: 1000
-    precision: 32
-    terminate_on_nan: false
-    check_val_every_n_epoch: 1
-    num_sanity_val_steps: 8
-    limit_val_batches: 1000
-    replace_sampler_ddp: false
-  checkpoint_kwargs:
-    verbose: true
-    save_top_k: 5
-    save_last: true
-    period: 1
-    monitor: val_ssim_fid100_f1_total_mean
-    mode: max

extensions-builtin/forge_preprocessor_inpaint/scripts/preprocessor_inpaint.py

@@ -1,219 +0,0 @@
-import os
-import cv2
-import torch
-import numpy as np
-import yaml
-import einops
-
-from omegaconf import OmegaConf
-from modules_forge.supported_preprocessor import Preprocessor, PreprocessorParameter
-from modules_forge.forge_util import numpy_to_pytorch, resize_image_with_pad
-from modules_forge.shared import preprocessor_dir, add_supported_preprocessor
-from modules.modelloader import load_file_from_url
-from annotator.lama.saicinpainting.training.trainers import load_checkpoint
-
-
-class PreprocessorInpaint(Preprocessor):
-    def __init__(self):
-        super().__init__()
-        self.name = 'inpaint_global_harmonious'
-        self.tags = ['Inpaint']
-        self.model_filename_filters = ['inpaint']
-        self.slider_resolution = PreprocessorParameter(visible=False)
-        self.fill_mask_with_one_when_resize_and_fill = True
-        self.expand_mask_when_resize_and_fill = True
-
-    def process_before_every_sampling(self, process, cond, mask, *args, **kwargs):
-        mask = mask.round()
-        mixed_cond = cond * (1.0 - mask) - mask
-        return mixed_cond, None
-
-
-class PreprocessorInpaintOnly(PreprocessorInpaint):
-    def __init__(self):
-        super().__init__()
-        self.name = 'inpaint_only'
-        self.image = None
-        self.mask = None
-        self.latent = None
-
-    def process_before_every_sampling(self, process, cond, mask, *args, **kwargs):
-        mask = mask.round()
-        self.image = cond
-        self.mask = mask
-
-        vae = process.sd_model.forge_objects.vae
-        latent_image = vae.encode(self.image.movedim(1, -1))
-        latent_image = process.sd_model.forge_objects.unet.model.latent_format.process_in(latent_image)
-
-        B, C, H, W = latent_image.shape
-
-        latent_mask = self.mask
-        latent_mask = torch.nn.functional.interpolate(latent_mask, size=(H * 8, W * 8), mode="bilinear").round()
-        latent_mask = torch.nn.functional.max_pool2d(latent_mask, (8, 8)).round().to(latent_image)
-
-        unet = process.sd_model.forge_objects.unet.clone()
-
-        def pre_cfg(args):
-            x = args['input']
-            timestep = args['timestep']
-            noisy_latent = latent_image.to(x) + timestep[:, None, None, None].to(x) * torch.randn_like(latent_image).to(x)
-            x = x * latent_mask.to(x) + noisy_latent.to(x) * (1.0 - latent_mask.to(x))
-            args['input'] = x
-
-            return args['conds_out']
-
-        def post_cfg(args):
-            denoised = args['denoised']
-            denoised = denoised * latent_mask.to(denoised) + latent_image.to(denoised) * (1.0 - latent_mask.to(denoised))
-            return denoised
-
-        unet.set_model_sampler_pre_cfg_function(pre_cfg)
-        unet.set_model_sampler_post_cfg_function(post_cfg)
-
-        process.sd_model.forge_objects.unet = unet
-
-        self.latent = latent_image
-
-        mixed_cond = cond * (1.0 - mask) - mask
-
-        return mixed_cond, None
-
-    def process_after_every_sampling(self, process, params, *args, **kwargs):
-        a1111_batch_result = args[0]
-        new_results = []
-
-        for img in a1111_batch_result.images:
-            sigma = 7
-            mask = self.mask[0, 0].detach().cpu().numpy().astype(np.float32)
-            mask = cv2.dilate(mask, np.ones((sigma, sigma), dtype=np.uint8))
-            mask = cv2.blur(mask, (sigma, sigma))[None]
-            mask = torch.from_numpy(np.ascontiguousarray(mask).copy()).to(img).clip(0, 1)
-            raw = self.image[0].to(img).clip(0, 1)
-            img = img.clip(0, 1)
-            new_results.append(raw * (1.0 - mask) + img * mask)
-
-        a1111_batch_result.images = new_results
-        return
-
-
-class PreprocessorInpaintLama(PreprocessorInpaintOnly):
-    def __init__(self):
-        super().__init__()
-        self.name = 'inpaint_only+lama'
-
-    def load_model(self):
-        remote_model_path = "https://huggingface.co/lllyasviel/Annotators/resolve/main/ControlNetLama.pth"
-        model_path = load_file_from_url(remote_model_path, model_dir=preprocessor_dir)
-        config_path = os.path.join(os.path.dirname(os.path.abspath(__file__)), 'lama_config.yaml')
-        cfg = yaml.safe_load(open(config_path, 'rt'))
-        cfg = OmegaConf.create(cfg)
-        cfg.training_model.predict_only = True
-        cfg.visualizer.kind = 'noop'
-        model = load_checkpoint(cfg, os.path.abspath(model_path), strict=False, map_location='cpu')
-        self.setup_model_patcher(model)
-        return
-
-    def __call__(self, input_image, resolution, slider_1=None, slider_2=None, slider_3=None, input_mask=None, **kwargs):
-        if input_mask is None:
-            return input_image
-
-        H, W, C = input_image.shape
-        raw_color = input_image.copy()
-        raw_mask = input_mask.copy()
-
-        input_image, remove_pad = resize_image_with_pad(input_image, 256)
-        input_mask, remove_pad = resize_image_with_pad(input_mask, 256)
-        input_mask = input_mask[..., :1]
-
-        self.load_model()
-
-        self.move_all_model_patchers_to_gpu()
-
-        color = np.ascontiguousarray(input_image).astype(np.float32) / 255.0
-        mask = np.ascontiguousarray(input_mask).astype(np.float32) / 255.0
-        with torch.no_grad():
-            color = self.send_tensor_to_model_device(torch.from_numpy(color))
-            mask = self.send_tensor_to_model_device(torch.from_numpy(mask))
-            mask = (mask > 0.5).float()
-            color = color * (1 - mask)
-            image_feed = torch.cat([color, mask], dim=2)
-            image_feed = einops.rearrange(image_feed, 'h w c -> 1 c h w')
-            prd_color = self.model_patcher.model(image_feed)[0]
-            prd_color = einops.rearrange(prd_color, 'c h w -> h w c')
-            
-            # Ensure all tensors are on the same device
-            device = prd_color.device
-            mask = mask.to(device)
-            color = color.to(device)
-            
-            prd_color = prd_color * mask + color * (1 - mask)
-            prd_color *= 255.0
-            prd_color = prd_color.detach().cpu().numpy().clip(0, 255).astype(np.uint8)
-
-        prd_color = remove_pad(prd_color)
-        prd_color = cv2.resize(prd_color, (W, H))
-
-        alpha = raw_mask.astype(np.float32) / 255.0
-        fin_color = prd_color.astype(np.float32) * alpha + raw_color.astype(np.float32) * (1 - alpha)
-        fin_color = fin_color.clip(0, 255).astype(np.uint8)
-
-        return fin_color
-
-    def process_before_every_sampling(self, process, cond, mask, *args, **kwargs):
-        cond, mask = super().process_before_every_sampling(process, cond, mask, *args, **kwargs)
-        sigma_max = process.sd_model.forge_objects.unet.model.model_sampling.sigma_max
-        original_noise = kwargs['noise']
-        process.modified_noise = original_noise + self.latent.to(original_noise) / sigma_max.to(original_noise)
-        return cond, mask
-
-class PreprocessorInpaintNoobAIXL(Preprocessor):
-    def __init__(self):
-        super().__init__()
-        self.name = 'inpaint_noobai_xl'
-        self.tags = ['Inpaint']
-        self.model_filename_filters = ['inpaint', 'noobai']
-        self.slider_resolution = PreprocessorParameter(visible=False)
-        self.fill_mask_with_one_when_resize_and_fill = True
-        self.expand_mask_when_resize_and_fill = True
-
-    def __call__(self, input_image, resolution=512, slider_1=None, slider_2=None, slider_3=None, input_mask=None, **kwargs):
-        if input_mask is None:
-            return input_image
-            
-        if not isinstance(input_image, np.ndarray):
-            input_image = np.array(input_image)
-        if not isinstance(input_mask, np.ndarray):
-            input_mask = np.array(input_mask)
-            
-        mask = input_mask.astype(np.float32) / 255.0
-        mask = (mask > 0.5).astype(np.float32)
-            
-        # Create a copy of the input image
-        result = input_image.copy()
-        
-        # Convert mask to proper shape if needed
-        if mask.ndim == 2:
-            mask = np.expand_dims(mask, axis=-1)
-        if mask.shape[-1] == 1:
-            mask = np.repeat(mask, 3, axis=-1)
-            
-        mask_indices = mask > 0.5
-        result[mask_indices] = 0.0
-        
-        return result
-        
-    def process_before_every_sampling(self, process, cond, mask, *args, **kwargs):
-        mask = mask.round()
-        mixed_cond = cond.clone()
-        mixed_cond = mixed_cond * (1.0 - mask)
-        
-        return mixed_cond, None
-
-add_supported_preprocessor(PreprocessorInpaint())
-
-add_supported_preprocessor(PreprocessorInpaintOnly())
-
-add_supported_preprocessor(PreprocessorInpaintLama())
-
-add_supported_preprocessor(PreprocessorInpaintNoobAIXL())