bump version to 1.0.2

Update project description and logo

.github/workflows/publish.yml

@@ -0,0 +1,21 @@
+name: Publish to Comfy registry
+on:
+  workflow_dispatch:
+  push:
+    branches:
+      - main
+    paths:
+      - "pyproject.toml"
+
+jobs:
+  publish-node:
+    name: Publish Custom Node to registry
+    runs-on: ubuntu-latest
+    steps:
+      - name: Check out code
+        uses: actions/checkout@v4
+      - name: Publish Custom Node
+        uses: Comfy-Org/publish-node-action@main
+        with:
+          ## Add your own personal access token to your Github Repository secrets and reference it here.
+          personal_access_token: ${{ secrets.REGISTRY_ACCESS_TOKEN }}

README.md

@@ -0,0 +1,202 @@
+# ArtVenture Custom Nodes
+
+A comprehensive set of custom nodes for ComfyUI, focusing on utilities for image processing, JSON manipulation, model operations and working with object via URLs
+
+### Image Nodes
+
+#### LoadImageFromUrl
+
+Loads images from URLs.
+
+**Inputs:**
+
+- `image`: List of URLs or base64 image data, separated by new lines
+- `keep_alpha_channel`: Preserve alpha channel
+- `output_mode`: List or batch output. Use `List` if you have different resolutions.
+
+![load image from url](https://github.com/user-attachments/assets/9da4840c-925e-4e0c-984a-5412282aee79)
+
+### JSON Nodes
+
+#### LoadJsonFromUrl
+
+Loads JSON data from URLs.
+
+**Inputs:**
+
+- `url`: JSON URL
+- `print_to_console`: Print JSON to console
+
+#### LoadJsonFromText
+
+Loads JSON data from text.
+
+**Inputs:**
+
+- `data`: JSON text
+- `print_to_console`: Print JSON to console
+
+#### Get<\*>FromJson
+
+Includes `GetObjectFromJson`, `GetTextFromJson`, `GetFloatFromJson`, `GetIntFromJson`, `GetBoolFromJson`.
+
+Use key format `key.[index].subkey.[sub_index]` to access nested objects.
+
+![get data from json](https://github.com/user-attachments/assets/a71793d6-9661-441c-a15c-66b2dcaa7972)
+
+### Utility Nodes
+
+#### StringToNumber
+
+Converts strings to numbers.
+
+**Inputs:**
+
+- `string`: Input string
+- `rounding`: Rounding method
+
+#### TextRandomMultiline
+
+Randomizes the order of lines in a multiline string.
+
+**Inputs:**
+
+- `text`: Input text
+- `amount`: Number of lines to randomize
+- `seed`: Random seed
+
+![text random multiline](https://github.com/user-attachments/assets/86f811e3-579e-4ccc-81a3-e216cd851d3c)
+
+### Inpainting Nodes
+
+#### PrepareImageAndMaskForInpaint
+
+Prepares images and masks for inpainting operations. It's to mimic the behavior of the inpainting in A1111.
+
+**Inputs:**
+
+- `image`: Input image tensor
+- `mask`: Input mask tensor
+- `mask_blur`: Blur amount for mask (0-64)
+- `inpaint_masked`: Whether to inpaint only the masked regions, otherwise it will inpaint the whole image.
+- `mask_padding`: Padding around mask (0-256)
+- `width`: Manually set inpaint area width. Leave 0 default to the masked area plus padding. (0-2048)
+- `height`: Manually set inpaint area height. (0-2048)
+
+**Outputs:**
+
+- `inpaint_image`: Processed image for inpainting
+- `inpaint_mask`: Processed mask
+- `overlay_image`: Preview overlay
+- `crop_region`: Crop coordinates (input of OverlayInpaintedImage)
+
+![inpaiting prepare](https://github.com/user-attachments/assets/38e87c04-7a64-4a62-a462-054396b3de14)
+
+#### OverlayInpaintedImage
+
+Overlays inpainted images with original images.
+
+**Inputs:**
+
+- `inpainted`: Inpainted image
+- `overlay_image`: Original image
+- `crop_region`: Crop region coordinates
+
+**Outputs:**
+
+- `IMAGE`: Final composited image
+
+#### LaMaInpaint
+
+Remove objects from images using LaMa model.
+
+![lama remove object](https://github.com/user-attachments/assets/c28bbd8b-d55f-4fa5-bbc9-ace267382bd0)
+
+### LLM Nodes
+
+#### LLMApiConfig
+
+Configures generic LLM API parameters.
+
+**Inputs:**
+
+- `model`: Model name (GPT-3.5, GPT-4, etc)
+- `max_token`: Maximum tokens
+- `temperature`: Temperature parameter
+
+#### OpenAIApi
+
+Configures OpenAI API access.
+
+**Inputs:**
+
+- `openai_api_key`: OpenAI API key
+- `endpoint`: API endpoint URL
+
+### Claude API Nodes
+
+#### ClaudeApi
+
+Configures Anthropic Claude API access.
+
+**Inputs:**
+
+- `claude_api_key`: Claude API key
+- `endpoint`: API endpoint
+- `version`: API version
+
+#### AwsBedrockClaudeApi
+
+Configures AWS Bedrock Claude API access.
+
+**Inputs:**
+
+- `aws_access_key_id`: AWS access key
+- `aws_secret_access_key`: AWS secret key
+- `region`: AWS region
+- `version`: API version
+
+#### AwsBedrockMistralApi
+
+Configures AWS Bedrock Mistral API access.
+
+**Inputs:**
+
+- `aws_access_key_id`: AWS access key
+- `aws_secret_access_key`: AWS secret key
+- `region`: AWS region
+
+#### LLMMessage
+
+Creates a message for LLM interaction.
+
+**Inputs:**
+
+- `role`: Message role (system/user/assistant)
+- `text`: Message content
+- `image`: Optional image input
+- `messages`: Previous message history
+
+#### LLMChat
+
+Handles chat interactions with LLMs.
+
+**Inputs:**
+
+- `messages`: Message history
+- `api`: LLM API configuration
+- `config`: Model configuration
+- `seed`: Random seed
+
+#### LLMCompletion
+
+Handles completion requests to LLMs.
+
+**Inputs:**
+
+- `prompt`: Input prompt
+- `api`: LLM API configuration
+- `config`: Model configuration
+- `seed`: Random seed
+
+![Screenshot 2024-10-30 at 11 20 12](https://github.com/user-attachments/assets/45b8d4fd-57cd-4bd9-8274-d3e6ac4ef938)

modules/inpaint/lama/__init__.py

@@ -1,6 +1,5 @@
 # https://github.com/advimman/lama
 import os
-import yaml
 import torch
 import torch.nn.functional as F
 
@@ -8,15 +7,14 @@ import folder_paths
 import comfy.model_management as model_management
 
 from ...model_utils import download_model
-from ...utils import ensure_package
 
 
 lama = None
 gpu = model_management.get_torch_device()
 cpu = torch.device("cpu")
 model_dir = os.path.join(folder_paths.models_dir, "lama")
-model_url = "https://d111kwgh87c0gj.cloudfront.net/stable-diffusion/lama/big-lama.pt"
-config_path = os.path.join(os.path.dirname(os.path.realpath(__file__)), "config.yaml")
+model_url = "https://github.com/Sanster/models/releases/download/add_big_lama/big-lama.pt"
+model_sha = "344c77bbcb158f17dd143070d1e789f38a66c04202311ae3a258ef66667a9ea9"
 
 
 def ceil_modulo(x, mod):
@@ -35,11 +33,6 @@ def pad_tensor_to_modulo(img, mod):
 def load_model():
     global lama
     if lama is None:
-        ensure_package("omegaconf")
-
-        from omegaconf import OmegaConf
-        from .saicinpainting.training.trainers import load_checkpoint
-
         files = download_model(
             model_path=model_dir,
             model_url=model_url,
@@ -47,13 +40,8 @@ def load_model():
             download_name="big-lama.pt",
         )
 
-        cfg = yaml.safe_load(open(config_path, "rt"))
-        cfg = OmegaConf.create(cfg)
-        cfg.training_model.predict_only = True
-        cfg.visualizer.kind = "noop"
-
-        lama = load_checkpoint(cfg, files[0], strict=False, map_location="cpu")
-        lama.freeze()
+        lama = torch.jit.load(files[0], map_location="cpu")
+        lama.eval()
 
     return lama
 
@@ -98,13 +86,11 @@ class LaMaInpaint:
                 msk = (msk > 0) * 1.0
                 msk = msk.unsqueeze(0).unsqueeze(0)
 
-                batch = {}
-                batch["image"] = pad_tensor_to_modulo(img, 8).to(device)
-                batch["mask"] = pad_tensor_to_modulo(msk, 8).to(device)
+                src_image = pad_tensor_to_modulo(img, 8).to(device)
+                src_mask = pad_tensor_to_modulo(msk, 8).to(device)
 
-                res = model(batch)
-                res = batch["inpainted"][0].permute(1, 2, 0)
-                res = res.detach().cpu()
+                res = model(src_image, src_mask)
+                res = res[0].permute(1, 2, 0).detach().cpu()
                 res = res[:orig_h, :orig_w]
 
                 inpainted.append(res)

modules/inpaint/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

modules/inpaint/lama/saicinpainting/training/data/masks.py

@@ -1,367 +0,0 @@
-import math
-import random
-import hashlib
-import logging
-from enum import Enum
-
-import cv2
-import numpy as np
-
-# from ..evaluation.masks.mask import SegmentationMask
-from ...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=1.0, 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)

modules/inpaint/lama/saicinpainting/training/losses/adversarial.py

@@ -1,204 +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}")

modules/inpaint/lama/saicinpainting/training/losses/constants.py

@@ -1,154 +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,
-    ]
-}

modules/inpaint/lama/saicinpainting/training/losses/distance_weighting.py

@@ -1,133 +0,0 @@
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-import torchvision
-
-from .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}")

modules/inpaint/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

modules/inpaint/lama/saicinpainting/training/losses/perceptual.py

@@ -1,84 +0,0 @@
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-import torchvision
-
-from ...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

modules/inpaint/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

modules/inpaint/lama/saicinpainting/training/losses/style_loss.py

@@ -1,150 +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

modules/inpaint/lama/saicinpainting/training/modules/__init__.py

@@ -1,36 +0,0 @@
-import logging
-
-from ..modules.ffc import FFCResNetGenerator
-from ..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}")

modules/inpaint/lama/saicinpainting/training/modules/base.py

@@ -1,96 +0,0 @@
-import abc
-from typing import Tuple, List
-
-import torch
-import torch.nn as nn
-
-from .depthwise_sep_conv import DepthWiseSeperableConv
-from .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}")

modules/inpaint/lama/saicinpainting/training/modules/depthwise_sep_conv.py

@@ -1,18 +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

modules/inpaint/lama/saicinpainting/training/modules/fake_fakes.py

@@ -1,50 +0,0 @@
-import torch
-from kornia.constants 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

modules/inpaint/lama/saicinpainting/training/modules/ffc.py

@@ -1,589 +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 .base import get_activation, BaseDiscriminator
-from .spatial_transform import LearnableSpatialTransformWrapper
-from .squeeze_excitation import SELayer
-
-
-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
-
-        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):
-        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(ffted)
-            coords_hor = torch.linspace(0, 1, width)[None, None, None, :].expand(batch, 1, height, width).to(ffted)
-            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 SpectralTransform(nn.Module):
-    def __init__(self, in_channels, out_channels, stride=1, groups=1, enable_lfu=True, **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),
-        )
-        self.fu = FourierUnit(out_channels // 2, out_channels // 2, groups, **fu_kwargs)
-        if self.enable_lfu:
-            self.lfu = FourierUnit(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):
-        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)
-        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):
-        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)
-
-        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)
-
-
-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 forward(self, input):
-        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):
-        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

modules/inpaint/lama/saicinpainting/training/modules/multidilated_conv.py

@@ -1,117 +0,0 @@
-import torch
-import torch.nn as nn
-import random
-
-from .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

modules/inpaint/lama/saicinpainting/training/modules/multiscale.py

@@ -1,338 +0,0 @@
-from typing import List, Tuple, Union, Optional
-
-import torch
-import torch.nn as nn
-import torch.nn.functional as F
-
-from .base import get_conv_block_ctor, get_activation
-from .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

modules/inpaint/lama/saicinpainting/training/modules/pix2pixhd.py

@@ -1,893 +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 .base import BaseDiscriminator, deconv_factory, get_conv_block_ctor, get_norm_layer, get_activation
-from .ffc import FFCResnetBlock
-from .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]

modules/inpaint/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")

modules/inpaint/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

modules/inpaint/lama/saicinpainting/training/trainers/__init__.py

@@ -1,31 +0,0 @@
-import logging
-import torch
-
-from .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: torch.nn.Module = make_training_model(train_config)
-    state = torch.load(path, map_location=map_location)
-    model.load_state_dict(state["state_dict"], strict=strict)
-    model.on_load_checkpoint(state)
-    return model

modules/inpaint/lama/saicinpainting/training/trainers/base.py

@@ -1,316 +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 saicinpainting.evaluation import make_evaluator
-# from saicinpainting.training.data.datasets import make_default_train_dataloader, make_default_val_dataloader
-# from saicinpainting.training.losses.adversarial import make_discrim_loss
-# from saicinpainting.training.losses.perceptual import PerceptualLoss, ResNetPL
-from ..modules import make_generator  # , make_discriminator
-
-# from saicinpainting.training.visualizers import make_visualizer
-from ...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.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

modules/inpaint/lama/saicinpainting/training/trainers/default.py

@@ -1,230 +0,0 @@
-import logging
-import random
-import torch
-import torch.nn.functional as F
-from omegaconf import OmegaConf
-
-from ..losses.distance_weighting import make_mask_distance_weighter
-from ..losses.feature_matching import feature_matching_loss, masked_l1_loss
-from ..modules.fake_fakes import FakeFakesGenerator
-from .base import BaseInpaintingTrainingModule, make_multiscale_noise
-from ...utils import add_prefix_to_keys, get_ramp
-
-LOGGER = logging.getLogger(__name__)
-
-
-def ceil_modulo(x, mod):
-    if x % mod == 0:
-        return x
-    return (x // mod + 1) * mod
-
-
-def make_constant_area_crop_params(img_height, img_width, min_size=128, max_size=512, area=256 * 256, round_to_mod=16):
-    min_size = min(img_height, img_width, min_size)
-    max_size = min(img_height, img_width, max_size)
-    if random.random() < 0.5:
-        out_height = min(max_size, ceil_modulo(random.randint(min_size, max_size), round_to_mod))
-        out_width = min(max_size, ceil_modulo(area // out_height, round_to_mod))
-    else:
-        out_width = min(max_size, ceil_modulo(random.randint(min_size, max_size), round_to_mod))
-        out_height = min(max_size, ceil_modulo(area // out_width, round_to_mod))
-
-    start_y = random.randint(0, img_height - out_height)
-    start_x = random.randint(0, img_width - out_width)
-    return (start_y, start_x, out_height, out_width)
-
-
-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

modules/inpaint/lama/saicinpainting/training/visualizers/__init__.py

@@ -1,15 +0,0 @@
-import logging
-
-from saicinpainting.training.visualizers.directory import DirectoryVisualizer
-from 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}')

modules/inpaint/lama/saicinpainting/training/visualizers/base.py

@@ -1,75 +0,0 @@
-import abc
-from typing import Dict, List
-
-import numpy as np
-import torch
-import skimage.color as 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, 0.0, 0.0), outline_color=(1.0, 1.0, 1.0), 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)

modules/inpaint/lama/saicinpainting/training/visualizers/colors.py

@@ -1,95 +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="horizontal",
-        )
-
-    return randRGBcolors, random_colormap

modules/inpaint/lama/saicinpainting/training/visualizers/directory.py

@@ -1,41 +0,0 @@
-import os
-
-import cv2
-import numpy as np
-
-from .base import BaseVisualizer, visualize_mask_and_images_batch
-from ...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)

modules/inpaint/lama/saicinpainting/training/visualizers/noop.py

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

modules/inpaint/lama/saicinpainting/utils.py

@@ -1,172 +0,0 @@
-import bisect
-import functools
-import logging
-import numbers
-import os
-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 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

modules/ip_adapter_nodes.py

@@ -1,8 +1,5 @@
 import os
-import json
-import torch
-from typing import Dict, Tuple, List
-from pydantic import BaseModel
+from typing import Dict, Tuple
 
 import folder_paths
 import comfy.clip_vision
@@ -10,8 +7,7 @@ import comfy.controlnet
 import comfy.utils
 import comfy.model_management
 
-from .utils import load_module, pil2tensor
-from .utility_nodes import load_images_from_url
+from .utils import load_module
 
 custom_nodes = folder_paths.get_folder_paths("custom_nodes")
 ip_adapter_dir_names = ["IPAdapter", "ComfyUI_IPAdapter_plus"]
@@ -36,42 +32,11 @@ try:
     print("Loaded IPAdapter nodes from", module_path)
 
     nodes: Dict = getattr(module, "NODE_CLASS_MAPPINGS")
-    IPAdapterUnifiedLoader = nodes.get("IPAdapterUnifiedLoader")
     IPAdapterModelLoader = nodes.get("IPAdapterModelLoader")
-    IPAdapterApply = nodes.get("IPAdapter")
-    IPAdapterEncoder = nodes.get("IPAdapterEncoder")
-    IPAdapterEmbeds = nodes.get("IPAdapterEmbeds")
-    IPAdapterCombineEmbeds = nodes.get("IPAdapterCombineEmbeds")
+    IPAdapterSimple = nodes.get("IPAdapter")
 
     loader = IPAdapterModelLoader()
-    unifyLoader = IPAdapterUnifiedLoader()
-    apply = IPAdapterApply()
-    encoder = IPAdapterEncoder()
-    combiner = IPAdapterCombineEmbeds()
-    embedder = IPAdapterEmbeds()
-
-    WEIGHT_TYPES = [
-        "linear",
-        "ease in",
-        "ease out",
-        "ease in-out",
-        "reverse in-out",
-        "weak input",
-        "weak output",
-        "weak middle",
-        "strong middle",
-        "style transfer (SDXL)",
-        "composition (SDXL)",
-    ]
-
-    PRESETS = [
-        "LIGHT - SD1.5 only (low strength)",
-        "STANDARD (medium strength)",
-        "VIT-G (medium strength)",
-        "PLUS (high strength)",
-        "PLUS FACE (portraits)",
-        "FULL FACE - SD1.5 only (portraits stronger)",
-    ]
+    apply = IPAdapterSimple()
 
     class AV_IPAdapterPipe:
         @classmethod
@@ -97,9 +62,11 @@ try:
             pipeline = {"ipadapter": {"model": ip_adapter}, "clipvision": {"model": clip_vision}}
             return (pipeline,)
 
-    class AV_IPAdapter(IPAdapterModelLoader, IPAdapterApply):
+    class AV_IPAdapter:
         @classmethod
         def INPUT_TYPES(cls):
+            inputs = IPAdapterSimple.INPUT_TYPES()
+
             return {
                 "required": {
                     "ip_adapter_name": (["None"] + folder_paths.get_filename_list("ipadapter"),),
@@ -107,7 +74,6 @@ try:
                     "model": ("MODEL",),
                     "image": ("IMAGE",),
                     "weight": ("FLOAT", {"default": 1.0, "min": -1, "max": 3, "step": 0.05}),
-                    "noise": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1.0, "step": 0.01}),
                 },
                 "optional": {
                     "ip_adapter_opt": ("IPADAPTER",),
@@ -115,10 +81,7 @@ try:
                     "attn_mask": ("MASK",),
                     "start_at": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1.0, "step": 0.001}),
                     "end_at": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.001}),
-                    "weight_type": (
-                        ["standard", "prompt is more important", "style transfer (SDXL only)"],
-                        {"default": "standard"},
-                    ),
+                    "weight_type": inputs["required"]["weight_type"],
                     "enabled": ("BOOLEAN", {"default": True}),
                 },
             }
@@ -135,7 +98,6 @@ try:
             model,
             image,
             weight,
-            noise,
             ip_adapter_opt=None,
             clip_vision_opt=None,
             enabled=True,
@@ -169,91 +131,16 @@ try:
 
             return res
 
-    class IPAdapterImage(BaseModel):
-        url: str
-        weight: float
-
-    class IPAdapterData(BaseModel):
-        images: List[IPAdapterImage]
-
-    class AV_StyleApply:
-        @classmethod
-        def INPUT_TYPES(cls):
-            return {
-                "required": {
-                    "model": ("MODEL",),
-                    "preset": (PRESETS,),
-                    "data": (
-                        "STRING",
-                        {
-                            "placeholder": '[{"url": "http://domain/path/image.png", "weight": 1}]',
-                            "multiline": True,
-                            "dynamicPrompts": False,
-                        },
-                    ),
-                    "weight": ("FLOAT", {"default": 0.5, "min": -1, "max": 3, "step": 0.05}),
-                    "weight_type": (WEIGHT_TYPES,),
-                    "start_at": ("FLOAT", {"default": 0.0, "min": 0.0, "max": 1.0, "step": 0.001}),
-                    "end_at": ("FLOAT", {"default": 1.0, "min": 0.0, "max": 1.0, "step": 0.001}),
-                },
-                "optional": {
-                    "mask": ("MASK",),
-                    "enabled": ("BOOLEAN", {"default": True}),
-                },
-            }
-
-        RETURN_TYPES = ("MODEL", "IMAGE")
-        CATEGORY = "Art Venture/Style"
-        FUNCTION = "apply_style"
-
-        def apply_style(self, model, preset: str, data: str, mask=None, enabled=True, **kwargs):
-            data = json.loads(data or "[]")
-            data: IPAdapterData = IPAdapterData(images=data)  # validate
-
-            if len(data.images) == 0:
-                images = torch.zeros((1, 64, 64, 3))
-                return (model, images)
-
-            (model, pipeline) = unifyLoader.load_models(model, preset)
-
-            urls = [image.url for image in data.images]
-            pils, _ = load_images_from_url(urls)
-
-            embeds_avg = None
-            neg_embeds_avg = None
-            images = []
-
-            for i, pil in enumerate(pils):
-                weight = data.images[i].weight
-                image = pil2tensor(pil)
-                if i > 0 and image.shape[1:] != images[0].shape[1:]:
-                    image = comfy.utils.common_upscale(
-                        image.movedim(-1, 1), images[0].shape[2], images[0].shape[1], "bilinear", "center"
-                    ).movedim(1, -1)
-                images.append(image)
-
-                embeds = encoder.encode(pipeline, image, weight, mask=mask)
-                if embeds_avg is None:
-                    embeds_avg = embeds[0]
-                    neg_embeds_avg = embeds[1]
-                else:
-                    embeds_avg = combiner.batch(embeds_avg, method="average", embed2=embeds[0])[0]
-                    neg_embeds_avg = combiner.batch(neg_embeds_avg, method="average", embed2=embeds[1])[0]
-
-            images = torch.cat(images)
-
-            model = embedder.apply_ipadapter(model, pipeline, embeds_avg, neg_embed=neg_embeds_avg, **kwargs)[0]
-
-            return (model, images)
-
     NODE_CLASS_MAPPINGS.update(
-        {"AV_IPAdapter": AV_IPAdapter, "AV_IPAdapterPipe": AV_IPAdapterPipe, "AV_StyleApply": AV_StyleApply}
+        {
+            "AV_IPAdapter": AV_IPAdapter,
+            "AV_IPAdapterPipe": AV_IPAdapterPipe,
+        }
     )
     NODE_DISPLAY_NAME_MAPPINGS.update(
         {
             "AV_IPAdapter": "IP Adapter Apply",
             "AV_IPAdapterPipe": "IP Adapter Pipe",
-            "AV_StyleApply": "AV Style Apply",
         }
     )
 

modules/utility_nodes.py

@@ -43,8 +43,8 @@ def prepare_image_for_preview(image: Image.Image, output_dir: str, prefix=None):
 
 
 def load_images_from_url(urls: List[str], keep_alpha_channel=False):
-    images = []
-    masks = []
+    images: List[Image.Image] = []
+    masks: List[Image.Image] = []
 
     for url in urls:
         if url.startswith("data:image/"):
@@ -105,14 +105,8 @@ def load_images_from_url(urls: List[str], keep_alpha_channel=False):
         if has_alpha:
             mask = i.getchannel("A")
 
-            # recreate image to fix weird RGB image
-            alpha = i.split()[-1]
-            image = Image.new("RGB", i.size, (0, 0, 0))
-            image.paste(i, mask=alpha)
-            image.putalpha(alpha)
-
-            if not keep_alpha_channel:
-                image = image.convert("RGB")
+        if not keep_alpha_channel:
+            image = i.convert("RGB")
         else:
             image = i
 
@@ -168,17 +162,22 @@ class UtilLoadImageFromUrl:
         np_masks = []
 
         for image, mask in zip(images, masks):
-            # save image to temp folder
-            preview = prepare_image_for_preview(image, self.output_dir, self.filename_prefix)
-            image = pil2tensor(image)
+            if mask is not None:
+                preview_image = Image.new("RGB", image.size)
+                preview_image.paste(image, (0, 0))
+                preview_image.putalpha(mask)
+            else:
+                preview_image = image
 
+            previews.append(prepare_image_for_preview(preview_image, self.output_dir, self.filename_prefix))
+
+            image = pil2tensor(image)
             if mask:
                 mask = np.array(mask).astype(np.float32) / 255.0
                 mask = 1.0 - torch.from_numpy(mask)
             else:
                 mask = torch.zeros((64, 64), dtype=torch.float32, device="cpu")
 
-            previews.append(preview)
             np_images.append(image)
             np_masks.append(mask.unsqueeze(0))
 
@@ -227,20 +226,25 @@ class UtilLoadImageAsMaskFromUrl(UtilLoadImageFromUrl):
         urls = image.strip().split("\n")
         images, alphas = load_images_from_url(urls, True)
 
-        masks = []
+        masks: List[torch.Tensor] = []
 
-        for image, alpha in zip(images, alphas):
+        for img, alpha in zip(images, alphas):
             if channel == "alpha":
                 mask = alpha
             elif channel == "red":
-                mask = image.getchannel("R")
+                mask = img.getchannel("R")
             elif channel == "green":
-                mask = image.getchannel("G")
+                mask = img.getchannel("G")
             elif channel == "blue":
-                mask = image.getchannel("B")
+                mask = img.getchannel("B")
 
-            mask = np.array(mask).astype(np.float32) / 255.0
-            mask = 1.0 - torch.from_numpy(mask)
+            if mask:
+                mask = np.array(mask, dtype=np.float32) / 255.0
+                mask = torch.from_numpy(mask)
+                if channel == "alpha":
+                    mask = 1. - mask
+            else:
+                mask = torch.zeros((64, 64), dtype=torch.float32, device="cpu")
 
             masks.append(mask.unsqueeze(0))
 
@@ -338,7 +342,7 @@ class UtilGetTextFromJson:
     OUTPUT_NODE = True
 
     def get_string_from_json(self, json: Dict, key: str):
-        return (get_dict_attribute(json, key, ""),)
+        return (str(get_dict_attribute(json, key, "")),)
 
 
 class UtilGetFloatFromJson:
@@ -357,7 +361,7 @@ class UtilGetFloatFromJson:
     OUTPUT_NODE = True
 
     def get_float_from_json(self, json: Dict, key: str):
-        return (get_dict_attribute(json, key, 0.0),)
+        return (float(get_dict_attribute(json, key, 0.0)),)
 
 
 class UtilGetIntFromJson:
@@ -376,7 +380,7 @@ class UtilGetIntFromJson:
     OUTPUT_NODE = True
 
     def get_int_from_json(self, json: Dict, key: str):
-        return (get_dict_attribute(json, key, 0),)
+        return (int(get_dict_attribute(json, key, 0)),)
 
 
 class UtilGetBoolFromJson:

modules/utils.py

@@ -76,7 +76,19 @@ def get_dict_attribute(dict_inst: dict, name_string: str, default=None):
     value = dict_inst
 
     for key in nested_keys:
-        value = value.get(key, None)
+        # Handle array indexing
+        if key.startswith("[") and key.endswith("]"):
+            try:
+                index = int(key[1:-1])
+                if not isinstance(value, (list, tuple)) or index >= len(value):
+                    return default
+                value = value[index]
+            except (ValueError, TypeError):
+                return default
+        else:
+            if not isinstance(value, dict):
+                return default
+            value = value.get(key, None)
 
         if value is None:
             return default

pyproject.toml

@@ -1,15 +1,15 @@
 [project]
 name = "comfyui-art-venture"
-description = "Nodes: ImagesConcat, LoadImageFromUrl, AV_UploadImage"
-version = "1.0.0"
+description = "A comprehensive set of custom nodes for ComfyUI, focusing on utilities for image processing, JSON manipulation, model operations and working with object via URLs"
+version = "1.0.2"
 license = "LICENSE"
-dependencies = ["timm==0.6.13", "transformers", "fairscale", "pycocoevalcap", "opencv-python", "qrcode[pil]", "pytorch_lightning", "kornia", "pydantic", "segment_anything", "omegaconf", "boto3>=1.34.101"]
+dependencies = ["timm==0.6.13", "transformers", "fairscale", "pycocoevalcap", "opencv-python", "qrcode[pil]", "pytorch_lightning", "kornia", "pydantic", "segment_anything", "boto3>=1.34.101"]
 
 [project.urls]
 Repository = "https://github.com/sipherxyz/comfyui-art-venture"
 #  Used by Comfy Registry https://comfyregistry.org
 
 [tool.comfy]
-PublisherId = ""
-DisplayName = "comfyui-art-venture"
-Icon = ""
+PublisherId = "protogaia"
+DisplayName = "ComfyUI ArtVenture"
+Icon = "https://cdn.protogaia.com/assets/gaia.png"