diff --git a/MiAlgo/Dockerfile b/MiAlgo/Dockerfile new file mode 100644 index 0000000000000000000000000000000000000000..6c809d311b7e72a759f6710a6a5b6ac93f81b176 --- /dev/null +++ b/MiAlgo/Dockerfile @@ -0,0 +1,9 @@ +FROM q935970314/mialgo:2022 + +COPY . /workdir/ + +RUN export PATH=/opt/conda/bin:/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin && pip install grayness_index_python + +WORKDIR /workdir/ + + diff --git a/MiAlgo/README.txt b/MiAlgo/README.txt new file mode 100644 index 0000000000000000000000000000000000000000..3d79e7dd0d17de7c5bb0a3c4a980bc8e9927f4be --- /dev/null +++ b/MiAlgo/README.txt @@ -0,0 +1,16 @@ +Run the code to build docker and process the night image: + +docker build -t mialgo24 . + +cd DATA_FOLDER_PATH +docker run -it --rm --gpus all -v $(pwd)/data:/data mialgo24 ./run.sh + + +Please note that the "--gpus all" parameter is used and ensure that the model is running on the GPU. +We first load all images into memory. If the memory explodes, please process less than 50 images at a time. + +We will open source the code after the competition ends. + +If you have any questions, please contact me in time, thank you. + +Email:liushuai21@xiaomi.com diff --git a/MiAlgo/__pycache__/net_torch.cpython-36.pyc b/MiAlgo/__pycache__/net_torch.cpython-36.pyc new file mode 100644 index 0000000000000000000000000000000000000000..a0a87f5abb76e03805ab7f663157fc0079faf538 Binary files /dev/null and b/MiAlgo/__pycache__/net_torch.cpython-36.pyc differ diff --git a/MiAlgo/__pycache__/network.cpython-36.pyc b/MiAlgo/__pycache__/network.cpython-36.pyc new file mode 100644 index 0000000000000000000000000000000000000000..7055647398ad277b1407b94bbc4ec8c95cc0afcc Binary files /dev/null and b/MiAlgo/__pycache__/network.cpython-36.pyc differ diff --git a/MiAlgo/__pycache__/network.cpython-37.pyc b/MiAlgo/__pycache__/network.cpython-37.pyc new file mode 100644 index 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a/MiAlgo/auxiliary/__pycache__/utils.cpython-37.pyc b/MiAlgo/auxiliary/__pycache__/utils.cpython-37.pyc new file mode 100644 index 0000000000000000000000000000000000000000..370f4c8dad02a2616044406a129a5a29355cee71 Binary files /dev/null and b/MiAlgo/auxiliary/__pycache__/utils.cpython-37.pyc differ diff --git a/MiAlgo/auxiliary/__pycache__/utils.cpython-38.pyc b/MiAlgo/auxiliary/__pycache__/utils.cpython-38.pyc new file mode 100644 index 0000000000000000000000000000000000000000..fee6b45e619647cf6e4ad50582405cf542bd66a7 Binary files /dev/null and b/MiAlgo/auxiliary/__pycache__/utils.cpython-38.pyc differ diff --git a/MiAlgo/auxiliary/settings.py b/MiAlgo/auxiliary/settings.py new file mode 100644 index 0000000000000000000000000000000000000000..301e1058b5cf9b2bc477e273ef537cf2aa1aef81 --- /dev/null +++ b/MiAlgo/auxiliary/settings.py @@ -0,0 +1,47 @@ +import re + +import numpy as np +import torch + + +# --- Determinism (for reproducibility) --- + +def make_deterministic(seed: int): + torch.manual_seed(seed) + np.random.seed(seed) + torch.backends.cudnn.benchmark = False + + +# --- Device (cpu or cuda:n) --- + +DEVICE_TYPE = "cuda:0" + + +def get_device() -> torch.device: + if DEVICE_TYPE == "cpu": + print("\n Running on device 'cpu' \n") + return torch.device("cpu") + + if re.match(r"\bcuda:\b\d+", DEVICE_TYPE): + if not torch.cuda.is_available(): + print("\n WARNING: running on cpu since device {} is not available \n".format(DEVICE_TYPE)) + return torch.device("cpu") + + # print("\n Running on device '{}' \n".format(DEVICE_TYPE)) + return torch.device(DEVICE_TYPE) + + raise ValueError("ERROR: {} is not a valid device! Supported device are 'cpu' and 'cuda:n'".format(DEVICE_TYPE)) + + +DEVICE = get_device() + +# --- Model --- + +# If set to False, a simpler summation pooling will be used +USE_CONFIDENCE_WEIGHTED_POOLING = True +if not USE_CONFIDENCE_WEIGHTED_POOLING: + print("\n WARN: confidence-weighted pooling option is set to False \n") + +# Input size +TRAIN_IMG_W, TRAIN_IMG_H = 512, 512 +TEST_IMG_W, TEST_IMG_H = 0, 0 diff --git a/MiAlgo/auxiliary/utils.py b/MiAlgo/auxiliary/utils.py new file mode 100644 index 0000000000000000000000000000000000000000..59a66a0f755d43fb35a05f3c2adac37fbfaeff0b --- /dev/null +++ b/MiAlgo/auxiliary/utils.py @@ -0,0 +1,107 @@ +import math +import os +from typing import Union, List, Tuple + +import numpy as np +import torch +import torchvision.transforms.functional as F +from PIL.Image import Image +from scipy.spatial.distance import jensenshannon +from torch import Tensor +from torch.nn.functional import interpolate + +from auxiliary.settings import DEVICE + + + +def print_metrics(current_metrics: dict, best_metrics: dict): + print(" Mean ......... : {:.4f} (Best: {:.4f})".format(current_metrics["mean"], best_metrics["mean"])) + print(" Median ....... : {:.4f} (Best: {:.4f})".format(current_metrics["median"], best_metrics["median"])) + print(" Trimean ...... : {:.4f} (Best: {:.4f})".format(current_metrics["trimean"], best_metrics["trimean"])) + print(" Best 25% ..... : {:.4f} (Best: {:.4f})".format(current_metrics["bst25"], best_metrics["bst25"])) + print(" Worst 25% .... : {:.4f} (Best: {:.4f})".format(current_metrics["wst25"], best_metrics["wst25"])) + print(" Worst 5% ..... : {:.4f} (Best: {:.4f})".format(current_metrics["wst5"], best_metrics["wst5"])) + + +def correct(img: Image, illuminant: Tensor) -> Image: + """ + Corrects the color of the illuminant of a linear image based on an estimated (linear) illuminant + @param img: a linear image + @param illuminant: a linear illuminant + @return: a non-linear color-corrected version of the input image + """ + img = F.to_tensor(img).to(DEVICE) + + # Correct the image + correction = illuminant.unsqueeze(2).unsqueeze(3) * torch.sqrt(Tensor([3])).to(DEVICE) + corrected_img = torch.div(img, correction + 1e-10) + + # Normalize the image + max_img = torch.max(torch.max(torch.max(corrected_img, dim=1)[0], dim=1)[0], dim=1)[0] + 1e-10 + max_img = max_img.unsqueeze(1).unsqueeze(1).unsqueeze(1) + normalized_img = torch.div(corrected_img, max_img) + + return F.to_pil_image(linear_to_nonlinear(normalized_img).squeeze(), mode="RGB") + + +def linear_to_nonlinear(img: Union[np.array, Image, Tensor]) -> Union[np.array, Image, Tensor]: + if isinstance(img, np.ndarray): + return np.power(img, (1.0 / 2.2)) + if isinstance(img, Tensor): + return torch.pow(img, 1.0 / 2.2) + return F.to_pil_image(torch.pow(F.to_tensor(img), 1.0 / 2.2).squeeze(), mode="RGB") + + +def normalize(img: np.ndarray) -> np.ndarray: + max_int = 65535.0 + return np.clip(img, 0.0, max_int) * (1.0 / max_int) + + +def rgb_to_bgr(x: np.ndarray) -> np.ndarray: + return x[::-1] + + +def bgr_to_rgb(x: np.ndarray) -> np.ndarray: + return x[:, :, ::-1] + + +def hwc_to_chw(x: np.ndarray) -> np.ndarray: + """ Converts an image from height x width x channels to channels x height x width """ + return x.transpose(2, 0, 1) + + +def scale(x: Tensor) -> Tensor: + """ Scales all values of a tensor between 0 and 1 """ + x = x - x.min() + x = x / x.max() + return x + + +def rescale(x: Tensor, size: Tuple) -> Tensor: + """ Rescale tensor to image size for better visualization """ + return interpolate(x, size, mode='bilinear') + + +def angular_error(x: Tensor, y: Tensor, safe_v: float = 0.999999) -> Tensor: + x, y = torch.nn.functional.normalize(x, dim=1), torch.nn.functional.normalize(y, dim=1) + dot = torch.clamp(torch.sum(x * y, dim=1), -safe_v, safe_v) + angle = torch.acos(dot) * (180 / math.pi) + return torch.mean(angle).item() + + +def tvd(pred: Tensor, label: Tensor) -> Tensor: + """ + Total Variation Distance (TVD) is a distance measure for probability distributions + https://en.wikipedia.org/wiki/Total_variation_distance_of_probability_measures + """ + return (Tensor([0.5]) * torch.abs(pred - label)).sum() + + +def jsd(p: List, q: List) -> float: + """ + Jensen-Shannon Divergence (JSD) between two probability distributions as square of scipy's JS distance. Refs: + - https://docs.scipy.org/doc/scipy/reference/generated/scipy.spatial.distance.jensenshannon.html + - https://stackoverflow.com/questions/15880133/jensen-shannon-divergence + """ + return jensenshannon(p, q) ** 2 + diff --git a/MiAlgo/checkpoint/nn_enhance.pth b/MiAlgo/checkpoint/nn_enhance.pth new file mode 100644 index 0000000000000000000000000000000000000000..8a818c1a89c25b7ac7517581b511a841e06fb04c --- /dev/null +++ b/MiAlgo/checkpoint/nn_enhance.pth @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:37c37648922f149ebefcec8b4c32c687bbafacd1efd99e288b235aab6e834ad5 +size 152303133 diff --git a/MiAlgo/checkpoint/raw_denoise.pth b/MiAlgo/checkpoint/raw_denoise.pth new file mode 100644 index 0000000000000000000000000000000000000000..d16cabed996779fbdf21c6b669b55ba9017bbf03 --- /dev/null +++ b/MiAlgo/checkpoint/raw_denoise.pth @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:a987213026b43e214196154494e5b54045721dea11dcb8e6f1f2d8e632445699 +size 2072446 diff --git a/MiAlgo/classes/core/Evaluator.py b/MiAlgo/classes/core/Evaluator.py new file mode 100644 index 0000000000000000000000000000000000000000..3228927d1832c40bf10bc8ef32501c67660ff4e1 --- /dev/null +++ b/MiAlgo/classes/core/Evaluator.py @@ -0,0 +1,49 @@ +import numpy as np + + +class Evaluator: + + def __init__(self): + monitored_metrics = ["mean", "median", "trimean", "bst25", "wst25", "wst5"] + self.__metrics = {} + self.__best_metrics = {m: 100.0 for m in monitored_metrics} + self.__errors = [] + + def add_error(self, error: float): + self.__errors.append(error) + + def reset_errors(self): + self.__errors = [] + + def get_errors(self) -> list: + return self.__errors + + def get_metrics(self) -> dict: + return self.__metrics + + def get_best_metrics(self) -> dict: + return self.__best_metrics + + def compute_metrics(self) -> dict: + self.__errors = sorted(self.__errors) + self.__metrics = { + "mean": np.mean(self.__errors), + "median": self.__g(0.5), + "trimean": 0.25 * (self.__g(0.25) + 2 * self.__g(0.5) + self.__g(0.75)), + "bst25": np.mean(self.__errors[:int(0.25 * len(self.__errors))]), + "wst25": np.mean(self.__errors[int(0.75 * len(self.__errors)):]), + "wst5": self.__g(0.95) + } + return self.__metrics + + def update_best_metrics(self) -> dict: + self.__best_metrics["mean"] = self.__metrics["mean"] + self.__best_metrics["median"] = self.__metrics["median"] + self.__best_metrics["trimean"] = self.__metrics["trimean"] + self.__best_metrics["bst25"] = self.__metrics["bst25"] + self.__best_metrics["wst25"] = self.__metrics["wst25"] + self.__best_metrics["wst5"] = self.__metrics["wst5"] + return self.__best_metrics + + def __g(self, f: float) -> float: + return np.percentile(self.__errors, f * 100) diff --git a/MiAlgo/classes/core/Loss.py b/MiAlgo/classes/core/Loss.py new file mode 100644 index 0000000000000000000000000000000000000000..243927761e927d90331c87280e40339f8e0e1fad --- /dev/null +++ b/MiAlgo/classes/core/Loss.py @@ -0,0 +1,13 @@ +import torch +from torch import Tensor + + +class Loss: + def __init__(self, device: torch.device): + self._device = device + + def _compute(self, *args, **kwargs) -> Tensor: + pass + + def __call__(self, *args, **kwargs): + return self._compute(*args).to(self._device) diff --git a/MiAlgo/classes/core/LossTracker.py b/MiAlgo/classes/core/LossTracker.py new file mode 100644 index 0000000000000000000000000000000000000000..a8edc029375597f0a2da925bb4fa206b8b4263ea --- /dev/null +++ b/MiAlgo/classes/core/LossTracker.py @@ -0,0 +1,16 @@ +class LossTracker(object): + + def __init__(self): + self.val, self.avg, self.sum, self.count = 0, 0, 0, 0 + + def reset(self): + self.val, self.avg, self.sum, self.count = 0, 0, 0, 0 + + def update(self, val: float, n: int = 1): + self.val = val + self.sum += val * n + self.count += n + self.avg = self.sum / self.count + + def get_loss(self) -> float: + return self.avg diff --git a/MiAlgo/classes/core/Model.py b/MiAlgo/classes/core/Model.py new file mode 100644 index 0000000000000000000000000000000000000000..0ea92e8cba2e4bc6d5d1bb5513556a1eb25d91d3 --- /dev/null +++ b/MiAlgo/classes/core/Model.py @@ -0,0 +1,43 @@ +import os + +import torch +from torch import Tensor + +from auxiliary.settings import DEVICE +from classes.losses.AngularLoss import AngularLoss + + +class Model: + def __init__(self): + self._device = DEVICE + self._criterion = AngularLoss(self._device) + self._optimizer = None + self._network = None + + def print_network(self): + print("\n----------------------------------------------------------\n") + print(self._network) + print("\n----------------------------------------------------------\n") + + def log_network(self, path_to_log: str): + open(os.path.join(path_to_log, "network.txt"), 'a+').write(str(self._network)) + + def get_loss(self, pred: Tensor, label: Tensor) -> Tensor: + return self._criterion(pred, label) + + def train_mode(self): + self._network = self._network.train() + + def evaluation_mode(self): + self._network = self._network.eval() + + def save(self, path_to_log: str): + torch.save(self._network.state_dict(), os.path.join(path_to_log, "model.pth")) + + def load(self, path_to_pretrained: str): + path_to_model = os.path.join(path_to_pretrained, "model.pth") + self._network.load_state_dict(torch.load(path_to_model, map_location=self._device)) + + def set_optimizer(self, learning_rate: float, optimizer_type: str = "adam"): + optimizers_map = {"adam": torch.optim.Adam, "rmsprop": torch.optim.RMSprop} + self._optimizer = optimizers_map[optimizer_type](self._network.parameters(), lr=learning_rate) diff --git a/MiAlgo/classes/core/__pycache__/Evaluator.cpython-36.pyc b/MiAlgo/classes/core/__pycache__/Evaluator.cpython-36.pyc new file mode 100644 index 0000000000000000000000000000000000000000..3ad407b6991c61581a7773e4686b18bb22cd6265 Binary 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os +from typing import Tuple + +import numpy as np +import scipy.io +import torch +import torch.utils.data as data + +from auxiliary.utils import normalize, bgr_to_rgb, linear_to_nonlinear, hwc_to_chw +from classes.data.DataAugmenter import DataAugmenter + + +class ColorCheckerDataset(data.Dataset): + + def __init__(self, train: bool = True, folds_num: int = 1): + + self.__train = train + self.__da = DataAugmenter() + + path_to_folds = os.path.join("dataset", "folds.mat") + path_to_metadata = os.path.join("dataset", "metadata.txt") + self.__path_to_data = os.path.join("dataset", "preprocessed", "numpy_data") + self.__path_to_label = os.path.join("dataset", "preprocessed", "numpy_labels") + + folds = scipy.io.loadmat(path_to_folds) + img_idx = folds["tr_split" if self.__train else "te_split"][0][folds_num][0] + + metadata = open(path_to_metadata, 'r').readlines() + self.__fold_data = [metadata[i - 1] for i in img_idx] + + def __getitem__(self, index: int) -> Tuple: + file_name = self.__fold_data[index].strip().split(' ')[1] + img = np.array(np.load(os.path.join(self.__path_to_data, file_name + '.npy')), dtype='float32') + illuminant = np.array(np.load(os.path.join(self.__path_to_label, file_name + '.npy')), dtype='float32') + + if self.__train: + img, illuminant = self.__da.augment(img, illuminant) + else: + img = self.__da.crop(img) + + img = hwc_to_chw(linear_to_nonlinear(bgr_to_rgb(normalize(img)))) + + img = torch.from_numpy(img.copy()) + illuminant = torch.from_numpy(illuminant.copy()) + + if not self.__train: + img = img.type(torch.FloatTensor) + + return img, illuminant, file_name + + def __len__(self) -> int: + return len(self.__fold_data) diff --git a/MiAlgo/classes/data/DataAugmenter.py b/MiAlgo/classes/data/DataAugmenter.py new file mode 100644 index 0000000000000000000000000000000000000000..5e5be07f62b94f48a2819df5baa5db35e48eb055 --- /dev/null +++ b/MiAlgo/classes/data/DataAugmenter.py @@ -0,0 +1,152 @@ +import math +import random + +import cv2 +import numpy as np + +from auxiliary.settings import TRAIN_IMG_H, TRAIN_IMG_W, TEST_IMG_H, TEST_IMG_W +from auxiliary.utils import rgb_to_bgr + + +class DataAugmenter: + + def __init__(self): + # Input Size of the fully-convolutional network (SqueezeNet) + self.__train_size = (TRAIN_IMG_H, TRAIN_IMG_W) + self.__test_size = (TEST_IMG_H, TEST_IMG_W) + + # Rotation angle + self.__angle = 60 + + # Patch scale + self.__scale = [0.1, 1.0] + + # Color rescaling + self.__color = 0.8 + + @staticmethod + def __rotate_image(image: np.ndarray, angle: float) -> np.ndarray: + """ + Rotates an OpenCV 2 / NumPy image about it's centre by the given angle (in degrees). + The returned image will be large enough to hold the entire new image, with a black background + """ + + # Get the image size (note: NumPy stores image matrices backwards) + image_size = (image.shape[1], image.shape[0]) + image_center = tuple(np.array(image_size) / 2) + + # Convert the OpenCV 3x2 rotation matrix to 3x3 + rot_mat = np.vstack([cv2.getRotationMatrix2D(image_center, angle, 1.0), [0, 0, 1]]) + + rot_mat_no_translate = np.matrix(rot_mat[0:2, 0:2]) + + image_w2, image_h2 = image_size[0] * 0.5, image_size[1] * 0.5 + + # Obtain the rotated coordinates of the image corners + rotated_coords = [ + (np.array([-image_w2, image_h2]) * rot_mat_no_translate).A[0], + (np.array([image_w2, image_h2]) * rot_mat_no_translate).A[0], + (np.array([-image_w2, -image_h2]) * rot_mat_no_translate).A[0], + (np.array([image_w2, -image_h2]) * rot_mat_no_translate).A[0] + ] + + # Find the size of the new image + x_coords = [pt[0] for pt in rotated_coords] + x_pos, x_neg = [x for x in x_coords if x > 0], [x for x in x_coords if x < 0] + + y_coords = [pt[1] for pt in rotated_coords] + y_pos, y_neg = [y for y in y_coords if y > 0], [y for y in y_coords if y < 0] + + right_bound, left_bound, top_bound, bot_bound = max(x_pos), min(x_neg), max(y_pos), min(y_neg) + new_w, new_h = int(abs(right_bound - left_bound)), int(abs(top_bound - bot_bound)) + + # We require a translation matrix to keep the image centred + trans_mat = np.matrix([[1, 0, int(new_w * 0.5 - image_w2)], [0, 1, int(new_h * 0.5 - image_h2)], [0, 0, 1]]) + + # Compute the transform for the combined rotation and translation + affine_mat = (np.matrix(trans_mat) * np.matrix(rot_mat))[0:2, :] + + # Apply the transform + return cv2.warpAffine(image, affine_mat, (new_w, new_h), flags=cv2.INTER_LINEAR) + + @staticmethod + def __largest_rotated_rect(w: float, h: float, angle: float) -> tuple: + """ + Given a rectangle of size w x h that has been rotated by 'angle' (in radians), computes the width and height of + the largest possible axis-aligned rectangle within the rotated rectangle. + + Original JS code by 'Andri' and Magnus Hoff from Stack Overflow. Converted to Python by Aaron Snoswell + """ + quadrant = int(math.floor(angle / (math.pi / 2))) & 3 + sign_alpha = angle if ((quadrant & 1) == 0) else math.pi - angle + alpha = (sign_alpha % math.pi + math.pi) % math.pi + + bb_w = w * math.cos(alpha) + h * math.sin(alpha) + bb_h = w * math.sin(alpha) + h * math.cos(alpha) + + length = h if (w < h) else w + d = length * math.cos(alpha) + + gamma = math.atan2(bb_w, bb_w) if (w < h) else math.atan2(bb_w, bb_w) + delta = math.pi - alpha - gamma + a = d * math.sin(alpha) / math.sin(delta) + + y = a * math.cos(gamma) + x = y * math.tan(gamma) + + return bb_w - 2 * x, bb_h - 2 * y + + def __crop_around_center(self, image: np.ndarray, width: float, height: float) -> np.ndarray: + """ Given a NumPy / OpenCV 2 image, crops it to the given width and height around it's centre point """ + + image_size = (image.shape[1], image.shape[0]) + image_center = (int(image_size[0] * 0.5), int(image_size[1] * 0.5)) + + width = image_size[0] if width > image_size[0] else width + height = image_size[1] if height > image_size[1] else height + + x1, x2 = int(image_center[0] - width * 0.5), int(image_center[0] + width * 0.5) + y1, y2 = int(image_center[1] - height * 0.5), int(image_center[1] + height * 0.5) + + return cv2.resize(image[y1:y2, x1:x2], self.__train_size) + + def __rotate_and_crop(self, image: np.ndarray, angle: float) -> np.ndarray: + width, height = image.shape[:2] + target_width, target_height = self.__largest_rotated_rect(width, height, math.radians(angle)) + return self.__crop_around_center(self.__rotate_image(image, angle), target_width, target_height) + + @staticmethod + def __random_flip(img: np.ndarray) -> np.ndarray: + """ Perform random left/right flip with probability 0.5 """ + if random.randint(0, 1): + img = img[:, ::-1] + return img.astype(np.float32) + + def augment(self, img: np.ndarray, illumination: np.ndarray) -> tuple: + scale = math.exp(random.random() * math.log(self.__scale[1] / self.__scale[0])) * self.__scale[0] + s = min(max(int(round(min(img.shape[:2]) * scale)), 10), min(img.shape[:2])) + + start_x = random.randrange(0, img.shape[0] - s + 1) + start_y = random.randrange(0, img.shape[1] - s + 1) + img = img[start_x:start_x + s, start_y:start_y + s] + + img = self.__rotate_and_crop(img, angle=(random.random() - 0.5) * self.__angle) + img = self.__random_flip(img) + + color_aug = np.zeros(shape=(3, 3)) + for i in range(3): + color_aug[i, i] = 1 + random.random() * self.__color - 0.5 * self.__color + img *= np.array([[[color_aug[0][0], color_aug[1][1], color_aug[2][2]]]], dtype=np.float32) + new_image = np.clip(img, 0, 65535) + + new_illuminant = np.zeros_like(illumination) + illumination = rgb_to_bgr(illumination) + for i in range(3): + for j in range(3): + new_illuminant[i] += illumination[j] * color_aug[i, j] + new_illuminant = rgb_to_bgr(np.clip(new_illuminant, 0.01, 100)) + + return new_image, new_illuminant + + def crop(self, img: np.ndarray, scale: float = 0.5) -> np.ndarray: + return cv2.resize(img, self.__test_size, fx=scale, fy=scale) diff --git a/MiAlgo/classes/data/__pycache__/ColorCheckerDataset.cpython-36.pyc b/MiAlgo/classes/data/__pycache__/ColorCheckerDataset.cpython-36.pyc new file mode 100644 index 0000000000000000000000000000000000000000..93545a56ee4d9b41ba5efbfb4362bf8ecfe73172 Binary files /dev/null and b/MiAlgo/classes/data/__pycache__/ColorCheckerDataset.cpython-36.pyc differ diff --git a/MiAlgo/classes/data/__pycache__/ColorCheckerDataset.cpython-37.pyc b/MiAlgo/classes/data/__pycache__/ColorCheckerDataset.cpython-37.pyc new file mode 100644 index 0000000000000000000000000000000000000000..5d5c1f565f005cb54d98b5e8ae7007155e9f350e Binary files /dev/null and b/MiAlgo/classes/data/__pycache__/ColorCheckerDataset.cpython-37.pyc differ diff --git a/MiAlgo/classes/data/__pycache__/ColorCheckerDataset.cpython-38.pyc b/MiAlgo/classes/data/__pycache__/ColorCheckerDataset.cpython-38.pyc new file mode 100644 index 0000000000000000000000000000000000000000..40f1cdd37350fad88d942528680237c7e699bbb6 Binary files /dev/null and b/MiAlgo/classes/data/__pycache__/ColorCheckerDataset.cpython-38.pyc differ diff --git a/MiAlgo/classes/data/__pycache__/DataAugmenter.cpython-36.pyc b/MiAlgo/classes/data/__pycache__/DataAugmenter.cpython-36.pyc new file mode 100644 index 0000000000000000000000000000000000000000..b2bee8fa9a2ada68377071a61d8b98726bb7659e Binary files /dev/null and b/MiAlgo/classes/data/__pycache__/DataAugmenter.cpython-36.pyc differ diff --git a/MiAlgo/classes/data/__pycache__/DataAugmenter.cpython-37.pyc b/MiAlgo/classes/data/__pycache__/DataAugmenter.cpython-37.pyc new file mode 100644 index 0000000000000000000000000000000000000000..34c509e94d26f670436302ac1955218cc72f641b Binary files /dev/null and b/MiAlgo/classes/data/__pycache__/DataAugmenter.cpython-37.pyc differ diff --git a/MiAlgo/classes/data/__pycache__/DataAugmenter.cpython-38.pyc b/MiAlgo/classes/data/__pycache__/DataAugmenter.cpython-38.pyc new file mode 100644 index 0000000000000000000000000000000000000000..53452df00e3f473cb110b2e196a01cac85e58f10 Binary files /dev/null and b/MiAlgo/classes/data/__pycache__/DataAugmenter.cpython-38.pyc differ diff --git a/MiAlgo/classes/fc4/FC4.py b/MiAlgo/classes/fc4/FC4.py new file mode 100644 index 0000000000000000000000000000000000000000..28272cbfa56702ad2c3e3423fe57e1ede6f13f63 --- /dev/null +++ b/MiAlgo/classes/fc4/FC4.py @@ -0,0 +1,63 @@ +from typing import Union + +import torch +from torch import nn, Tensor +from torch.nn.functional import normalize + +from auxiliary.settings import USE_CONFIDENCE_WEIGHTED_POOLING +from classes.fc4.squeezenet.SqueezeNetLoader import SqueezeNetLoader + +""" +FC4: Fully Convolutional Color Constancy with Confidence-weighted Pooling +* Original code: https://github.com/yuanming-hu/fc4 +* Paper: https://www.microsoft.com/en-us/research/publication/fully-convolutional-color-constancy-confidence-weighted-pooling/ +""" + + +class FC4(torch.nn.Module): + + def __init__(self, squeezenet_version: float = 1.1): + super().__init__() + + # SqueezeNet backbone (conv1-fire8) for extracting semantic features + squeezenet = SqueezeNetLoader(squeezenet_version).load(pretrained=True) + self.backbone = nn.Sequential(*list(squeezenet.children())[0][:12]) + + # Final convolutional layers (conv6 and conv7) to extract semi-dense feature maps + self.final_convs = nn.Sequential( + nn.MaxPool2d(kernel_size=2, stride=1, ceil_mode=True), + nn.Conv2d(512, 64, kernel_size=6, stride=1, padding=3), + nn.ReLU(inplace=True), + nn.Dropout(p=0.5), + nn.Conv2d(64, 4 if USE_CONFIDENCE_WEIGHTED_POOLING else 3, kernel_size=1, stride=1), + nn.ReLU(inplace=True) + ) + + def forward(self, x: Tensor) -> Union[tuple, Tensor]: + """ + Estimate an RGB colour for the illuminant of the input image + @param x: the image for which the colour of the illuminant has to be estimated + @return: the colour estimate as a Tensor. If confidence-weighted pooling is used, the per-path colour estimates + and the confidence weights are returned as well (used for visualizations) + """ + + x = self.backbone(x) + out = self.final_convs(x) + + # Confidence-weighted pooling: "out" is a set of semi-dense feature maps + if USE_CONFIDENCE_WEIGHTED_POOLING: + # Per-patch color estimates (first 3 dimensions) + rgb = normalize(out[:, :3, :, :], dim=1) + + # Confidence (last dimension) + confidence = out[:, 3:4, :, :] + + # Confidence-weighted pooling + pred = normalize(torch.sum(torch.sum(rgb * confidence, 2), 2), dim=1) + + return pred, rgb, confidence + + # Summation pooling + pred = normalize(torch.sum(torch.sum(out, 2), 2), dim=1) + + return pred diff --git a/MiAlgo/classes/fc4/ModelFC4.py b/MiAlgo/classes/fc4/ModelFC4.py new file mode 100644 index 0000000000000000000000000000000000000000..eb37b4aa79ee81098bcade69ff16188668e5b4d7 --- /dev/null +++ b/MiAlgo/classes/fc4/ModelFC4.py @@ -0,0 +1,44 @@ +import os +from typing import Union, Tuple + +import torchvision.transforms.functional as F +from torch import Tensor +from torchvision.transforms import transforms + +from auxiliary.settings import USE_CONFIDENCE_WEIGHTED_POOLING +from auxiliary.utils import correct, rescale, scale +from classes.core.Model import Model +from classes.fc4.FC4 import FC4 + + +class ModelFC4(Model): + + def __init__(self): + super().__init__() + self._network = FC4().to(self._device) + + def predict(self, img: Tensor, return_steps: bool = False) -> Union[Tensor, Tuple]: + """ + Performs inference on the input image using the FC4 method. + @param img: the image for which an illuminant colour has to be estimated + @param return_steps: whether or not to also return the per-patch estimates and confidence weights. When this + flag is set to True, confidence-weighted pooling must be active) + @return: the colour estimate as a Tensor. If "return_steps" is set to true, the per-path colour estimates and + the confidence weights are also returned (used for visualizations) + """ + if USE_CONFIDENCE_WEIGHTED_POOLING: + pred, rgb, confidence = self._network(img) + if return_steps: + return pred, rgb, confidence + return pred + return self._network(img) + + def optimize(self, img: Tensor, label: Tensor) -> float: + self._optimizer.zero_grad() + pred = self.predict(img) + loss = self.get_loss(pred, label) + loss.backward() + self._optimizer.step() + return loss.item() + + diff --git a/MiAlgo/classes/fc4/__pycache__/FC4.cpython-36.pyc b/MiAlgo/classes/fc4/__pycache__/FC4.cpython-36.pyc new file mode 100644 index 0000000000000000000000000000000000000000..97966c8c7a9cd57e76aa48112054f7ac19e0a42d Binary files /dev/null and b/MiAlgo/classes/fc4/__pycache__/FC4.cpython-36.pyc differ diff --git a/MiAlgo/classes/fc4/__pycache__/FC4.cpython-37.pyc b/MiAlgo/classes/fc4/__pycache__/FC4.cpython-37.pyc new file mode 100644 index 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self).__init__() + self.inplanes = inplanes + self.squeeze = nn.Conv2d(inplanes, squeeze_planes, kernel_size=1) + self.squeeze_activation = nn.ReLU(inplace=True) + self.expand1x1 = nn.Conv2d(squeeze_planes, expand1x1_planes, kernel_size=1) + self.expand1x1_activation = nn.ReLU(inplace=True) + self.expand3x3 = nn.Conv2d(squeeze_planes, expand3x3_planes, kernel_size=3, padding=1) + self.expand3x3_activation = nn.ReLU(inplace=True) + + def forward(self, x: Tensor) -> Tensor: + x = self.squeeze_activation(self.squeeze(x)) + return torch.cat([self.expand1x1_activation(self.expand1x1(x)), + self.expand3x3_activation(self.expand3x3(x))], 1) diff --git a/MiAlgo/classes/fc4/squeezenet/SqueezeNet.py b/MiAlgo/classes/fc4/squeezenet/SqueezeNet.py new file mode 100644 index 0000000000000000000000000000000000000000..6ee5272d73d4e5b06d2e49b8704e856e741ce502 --- /dev/null +++ b/MiAlgo/classes/fc4/squeezenet/SqueezeNet.py @@ -0,0 +1,82 @@ +import torch +import torch.nn as nn +import torch.nn.init as init + +from classes.fc4.squeezenet.Fire import Fire + +""" +This is the standard SqueezeNet implementation included in PyTorch at: +https://github.com/pytorch/vision/blob/072d8b2280569a2d13b91d3ed51546d201a57366/torchvision/models/squeezenet.py + +SqueezeNet 1.0 +-> Architecture from https://arxiv.org/abs/1602.07360, 'SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5MB model size'. + +SqueezeNet 1.1 (has 2.4x less computation and slightly fewer parameters than 1.0, without sacrificing accuracy) +-> Architecture from: `. +""" + + +class SqueezeNet(nn.Module): + + def __init__(self, version: float = 1.0, num_classes: int = 1000): + super().__init__() + + self.num_classes = num_classes + + if version == 1.0: + self.features = nn.Sequential( + nn.Conv2d(3, 96, kernel_size=7, stride=2), + nn.ReLU(inplace=True), + nn.MaxPool2d(kernel_size=3, stride=2, ceil_mode=True), + Fire(96, 16, 64, 64), + Fire(128, 16, 64, 64), + Fire(128, 32, 128, 128), + nn.MaxPool2d(kernel_size=3, stride=2, ceil_mode=True), + Fire(256, 32, 128, 128), + Fire(256, 48, 192, 192), + Fire(384, 48, 192, 192), + Fire(384, 64, 256, 256), + nn.MaxPool2d(kernel_size=3, stride=2, ceil_mode=True), + Fire(512, 64, 256, 256), + ) + elif version == 1.1: + self.features = nn.Sequential( + nn.Conv2d(3, 64, kernel_size=3, stride=2), + nn.ReLU(inplace=True), + nn.MaxPool2d(kernel_size=3, stride=2, ceil_mode=True), + Fire(64, 16, 64, 64), + Fire(128, 16, 64, 64), + nn.MaxPool2d(kernel_size=3, stride=2, ceil_mode=True), + Fire(128, 32, 128, 128), + Fire(256, 32, 128, 128), + nn.MaxPool2d(kernel_size=3, stride=2, ceil_mode=True), + Fire(256, 48, 192, 192), + Fire(384, 48, 192, 192), + Fire(384, 64, 256, 256), + Fire(512, 64, 256, 256), + ) + else: + raise ValueError("Unsupported SqueezeNet version {version}: 1.0 or 1.1 expected".format(version=version)) + + # Final convolution is initialized differently form the rest + final_conv = nn.Conv2d(512, self.num_classes, kernel_size=1) + self.classifier = nn.Sequential( + nn.Dropout(p=0.5), + final_conv, + nn.ReLU(inplace=True), + nn.AdaptiveAvgPool2d((1, 1)) + ) + + for m in self.modules(): + if isinstance(m, nn.Conv2d): + if m is final_conv: + init.normal_(m.weight, mean=0.0, std=0.01) + else: + init.kaiming_uniform_(m.weight) + if m.bias is not None: + init.constant_(m.bias, 0) + + def forward(self, x: torch): + x = self.features(x) + x = self.classifier(x) + return x.view(x.size(0), self.num_classes) diff --git a/MiAlgo/classes/fc4/squeezenet/SqueezeNetLoader.py b/MiAlgo/classes/fc4/squeezenet/SqueezeNetLoader.py new file mode 100644 index 0000000000000000000000000000000000000000..12fa5795bd104b31f2e88416f90d1e7c0dcf8f82 --- /dev/null +++ b/MiAlgo/classes/fc4/squeezenet/SqueezeNetLoader.py @@ -0,0 +1,27 @@ +import os + +from torch.utils import model_zoo + +from classes.fc4.squeezenet.SqueezeNet import SqueezeNet + +model_urls = { + 1.0: 'https://download.pytorch.org/models/squeezenet1_0-a815701f.pth', + 1.1: 'https://download.pytorch.org/models/squeezenet1_1-f364aa15.pth', +} + + +class SqueezeNetLoader: + def __init__(self, version: float = 1.1): + self.__version = version + self.__model = SqueezeNet(self.__version) + + def load(self, pretrained: bool = False) -> SqueezeNet: + """ + Returns the specified version of SqueezeNet + @param pretrained: if True, returns a model pre-trained on ImageNet + """ + if pretrained: + path_to_local = os.path.join("assets", "pretrained") + os.environ['TORCH_HOME'] = path_to_local + self.__model.load_state_dict(model_zoo.load_url(model_urls[self.__version])) + return self.__model diff --git a/MiAlgo/classes/fc4/squeezenet/__pycache__/Fire.cpython-36.pyc b/MiAlgo/classes/fc4/squeezenet/__pycache__/Fire.cpython-36.pyc new file mode 100644 index 0000000000000000000000000000000000000000..0edb19fbafbfc5607d19a3a84266583f2722855c Binary files /dev/null and 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+1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:3b5acaa5a014288e2e2c8230740fc2121351a92401ddf403e5e7fba16fa39204 +size 3145856 diff --git a/MiAlgo/lsc_npy/50.npy b/MiAlgo/lsc_npy/50.npy new file mode 100644 index 0000000000000000000000000000000000000000..b6030464e7d7119db7a6e7c9501c2d7ea6576d60 --- /dev/null +++ b/MiAlgo/lsc_npy/50.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:f3e679f54e94bdb3ef92717215e4b39c4786525a659bf756384fd21bd803e794 +size 3145856 diff --git a/MiAlgo/lsc_npy/640.npy b/MiAlgo/lsc_npy/640.npy new file mode 100644 index 0000000000000000000000000000000000000000..205d6f71b5b6576d3d1d2a9fd9e5b14c0f1d74a5 --- /dev/null +++ b/MiAlgo/lsc_npy/640.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:151e6b280dbecd20006c5a992d6c8080f37347e63a0c725f5b96f3318d376481 +size 3145856 diff --git a/MiAlgo/lsc_npy/800.npy b/MiAlgo/lsc_npy/800.npy new file mode 100644 index 0000000000000000000000000000000000000000..df8af28c99520d29500cd18d6b954f82f09c8ff2 --- /dev/null +++ b/MiAlgo/lsc_npy/800.npy @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:272c53ead6055a47403a43c1d9965293a3b20c08c0e058108a3f8c0b591a48c5 +size 3145856 diff --git a/MiAlgo/main_test.py b/MiAlgo/main_test.py new file mode 100644 index 0000000000000000000000000000000000000000..bcd1e9ed5f0820b03a0c56e7d474b6512a3819fa --- /dev/null +++ b/MiAlgo/main_test.py @@ -0,0 +1,719 @@ +import os + +import multiprocessing as mp +import numpy as np +import cv2 as cv +from tqdm import tqdm + +from glob import glob +from utils import * + +from grayness_index import GraynessIndex + +import torch +import torch.nn.functional as F + +from time import time + +from network_raw_denoise import sc_net_1f +from network import MWRCANv4 as NET +from classes.fc4.ModelFC4 import ModelFC4 + +def load_img(img_path): + meta_all = {} + meta_all['img_path'] = img_path + + # load meta + metadata = json_read(img_path.replace(".png", ".json"), object_hook=fraction_from_json) + meta_all['meta'] = metadata + + # load image + img = cv.imread(img_path, cv.IMREAD_UNCHANGED) + meta_all['img'] = img + + return meta_all + + + + +def pre_process(meta_all): + img = meta_all['img'] + + metadata = meta_all['meta'] + cfa_pattern = metadata['cfa_pattern'] + cfa_pattern_ = "" + for tt in cfa_pattern: + if tt == 0: + cfa_pattern_ += "r" + elif tt == 1: + cfa_pattern_ += "g" + elif tt == 2: + cfa_pattern_ += "b" + else: + raise + + offsets = bayer_to_offsets(cfa_pattern_) + img = pack_raw_to_4ch(img, offsets) + + if img.shape[0] != 768 and img.shape[1] != 1024: + img = cv.resize(img, (1024, 768), interpolation=cv.INTER_AREA) # RGB + + bl_fix = np.clip((float(metadata["noise_profile"][0])-0.005) * 1000, 0, 10) + img = normalize(img, metadata['black_level'], metadata['white_level'], bl_fix).astype(np.float32) + + noise_profile = float(metadata["noise_profile"][0]) + noise_list = [0.00025822882, 0.000580020745, 0.00141667975, 0.00278965863, 0.00347614807] + + if noise_profile < 0.005: + if noise_profile < noise_list[0]: + weight1 = noise_profile / noise_list[0] + final_lsc = lsc_npy[0] * weight1 + linear_idx1, linear_idx2 = 0, 0 + elif noise_profile > noise_list[-1]: + final_lsc = lsc_npy[-1] + linear_idx1, linear_idx2 = -1, -1 + else: + for idx, nn in enumerate(noise_list): + if noise_profile < nn: + linear_idx1 = idx - 1 + linear_idx2 = idx + break + + weight1 = (noise_profile - noise_list[linear_idx1]) / (noise_list[linear_idx2] - noise_list[linear_idx1]) + weight2 = 1-weight1 + final_lsc = lsc_npy[linear_idx1] * weight1 + lsc_npy[linear_idx2] * weight2 + + ones = np.ones_like(final_lsc) + final_lsc = final_lsc * 0.6 + ones * 0.4 + final_lsc[:, :512, :] = final_lsc[:, 1024:511:-1, :] + + img = img * final_lsc + + img = np.clip(img, 0.0, 1.0) + meta_all["img"] = img + + rgb_gain = metadata['as_shot_neutral'] + ra, ga, ba = rgb_gain + ra, ga, ba = 1/ra, 1/ga, 1/ba + + meta_all['r_gains'] = [ra] + meta_all['g_gains'] = [ga] + meta_all['b_gains'] = [ba] + + return meta_all + + + +def raw_denoise(results): + checkpoint_path = "checkpoint/raw_denoise.pth" + device = torch.device("cuda") + model = get_net(sc_net_1f, checkpoint_path, device) + + for meta_all in tqdm(results): + img = meta_all['img'] + + img = np.expand_dims(img, axis=0) + ori_inp = img.copy() + + clip_min = max(np.mean(img)*3, 0.9) + img = np.clip(img, 0, clip_min) + img = torch.from_numpy(img.transpose(0, 3, 1, 2)).cuda() + + with torch.no_grad(): + output = model(img) + + output = output.detach().cpu().numpy().transpose(0, 2, 3, 1) + + img = ori_inp + output + img = np.clip(img, 0, 1) + img = np.squeeze(img) + + meta_all['img'] = img + + + +def predict_white_balance(results): + model = ModelFC4() + for model_index in [0, 1, 2]: + path_to_pretrained = os.path.join("./trained_models", "fc4_cwp", "fold_{}".format(model_index)) + model.load(path_to_pretrained) + model.evaluation_mode() + + for meta_all in tqdm(results): + img = meta_all['img'].copy() + img[:, :, 1] = (img[:, :, 1] + img[:, :, 3]) / 2 + img = img[:, :, :-1] + + img = torch.from_numpy(img.transpose(2, 0, 1)).unsqueeze(0).cuda() + img_tmp = torch.pow(img, 1.0 / 2.2) + + with torch.no_grad(): + pred = model.predict(img_tmp, return_steps=False) + pred = pred.detach().cpu().squeeze(0).numpy() + + # rgb gain + r, g, b = pred + + r /= g + b /= g + g /= g + + r = 1./ r + g = 1./ g + b = 1./ b + + meta_all['r_gains'].append(r) + meta_all['g_gains'].append(g) + meta_all['b_gains'].append(b) + + +def convert_to_rgb(meta_all): + img = meta_all['img'] + img[:, :, 1] = (img[:, :, 1] + img[:, :, 3]) / 2 + img = img[:, :, :-1] + + + # WB + r_gains = sorted(meta_all['r_gains']) + b_gains = sorted(meta_all['b_gains']) + + r_final = (r_gains[0] + r_gains[1] + r_gains[2]) / 3 + g_final = 1 + b_final = (b_gains[1] + b_gains[2] + b_gains[3]) / 3 + + img[:, :, 0] *= r_final + img[:, :, 1] *= g_final + img[:, :, 2] *= b_final + + img = np.clip(img, 0, 1) + + + # CC + img = apply_color_space_transform(img, color_matrix, color_matrix) + + + # convert RGB + img = transform_xyz_to_srgb(img) + + + # shading fix + if float(meta_all['meta']["noise_profile"][0]) > 0.005: + lsc_m = lsc ** ((float(meta_all['meta']["noise_profile"][0])-0.005) * 100) + lsc_inv = 1 / lsc + lsc_inv = np.mean(lsc_inv, axis=-1, keepdims=True) + + gray = cv.cvtColor(img.astype(np.float32), cv.COLOR_RGB2GRAY) + gray = gray[:, :, np.newaxis] + + lsc_inv = lsc_inv * np.clip(gray*10, 0, 1) * np.clip((2 - (float(meta_all['meta']["noise_profile"][0])-0.005) * 100), 1, 2) + lsc_inv = np.clip(lsc_inv, 0.4, 1) + + img = img * lsc_inv + gray * (1-lsc_inv) + img = img / lsc_m + + + # tonemaping + img = apply_tone_map(img) + + + # gamma + img = apply_gamma(img).astype(np.float32) + img = np.clip(img, 0, 1) + + + # contrast enhancement + mm = np.mean(img) + meta_all['mm'] = mm + if mm <= 0.1: + pass + elif float(meta_all['meta']["noise_profile"][0]) > 0.01: + yuv = cv.cvtColor(img, cv.COLOR_BGR2YUV) + y, u, v = cv.split(yuv) + y = autocontrast_using_pil(y) + yuv = np.stack([y, u, v], axis=-1) + rgb = cv.cvtColor(yuv, cv.COLOR_YUV2BGR) + rgb = np.clip(rgb, 0, 1) + + img = img * 0.5 + rgb * 0.5 + + img = np.clip(img*255, 0, 255).round().astype(np.uint8) + + if float(meta_all['meta']["noise_profile"][0]) > 0.02: + noise_params = 6 + else: + noise_params = 3 + + img = cv.fastNlMeansDenoisingColored(img, None, noise_params, noise_params, 7, 21) + img = img.astype(np.float32) / 255. + + img = usm_sharp(img) + else: + img = autocontrast_using_pil(img) + + + # gamma again + img = np.clip(img, 0, 1) + img_con = img ** (1/1.5) + gray = np.max(img_con, axis=-1, keepdims=True) # - 0.1 + gray = np.clip(gray, 0.3, 1) + img = img_con * gray + img * (1-gray) + + + # AWB again + img = img[:, :, ::-1] # BGR + gi = GraynessIndex() + pred_illum = gi.apply(img) + r, g, b = pred_illum + pred_illum = pred_illum / g + r, g, b = pred_illum + if r < 1: + img = white_balance(img, pred_illum) # BGR + img = img[:, :, ::-1] + img = np.clip(img, 0, 1) # RGB + + + # fix orientation + img = fix_orientation(img, meta_all['meta']["orientation"]) + + meta_all['img'] = img # RGB + return meta_all + + +def nn_enhancement(results): + checkpoint_path1 = "checkpoint/nn_enhance.pth" + device = torch.device("cuda") + model = get_net(NET, checkpoint_path1, device) + + + for meta_all in tqdm(results): + # mm = meta_all['mm'] + # if mm <= 0.1 or float(meta_all['meta']["noise_profile"][0]) > 0.01: + # meta_all['img'] = meta_all['img'] * 255 + # continue + + img = meta_all['img'] + img = img.astype(np.float32) + img = torch.from_numpy(img.copy().transpose(2, 0, 1)).unsqueeze(0).to(device) + + with torch.no_grad(): + img = model(img) + # img = img + + img = img.detach().cpu().squeeze(0).numpy().transpose(1, 2, 0) + img = np.clip(img, 0, 1) + + img = img * 255. + img = img.round() + img = img.astype(np.uint8) + + meta_all['img'] = img # RGB U8 + + +def post_process(meta_all): + # color fix + img = meta_all['img'] # RGB U8 + + + # increase saturation + increment=0.5 + ori_img = img.copy() # RGB U8 + + hls = cv2.cvtColor(img, cv2.COLOR_RGB2HLS).astype(np.float32) + _, L, S = cv2.split(hls) + S = S / 255. + + img = img.astype(np.float32) + + temp = increment + S + mask_2 = temp > 1 # 大于1的位置 + alpha_1 = S + alpha_2 = 1 - increment + alpha = alpha_1 * mask_2 + alpha_2 * (1 - mask_2) + L = L[:, :, np.newaxis] + alpha = alpha[:, :, np.newaxis] + + alpha = 1/alpha -1 + + img = img + (img - L) * alpha + + img = np.clip(img, 0, 255) + + ori_img = ori_img.astype(np.float32) + mask = ori_img[:, :, 2] / 255. + # mask = np.max(ori_img, axis=-1) / 255. + mask = mask[:, :, np.newaxis] + mask = np.clip(mask - 0.1, 0, 1) + img = img * mask + ori_img * (1-mask) + img = np.clip(img, 0, 255).round().astype(np.uint8) + + + + # decrease saturation + hsv = cv2.cvtColor(img, cv2.COLOR_RGB2HLS) + hsv = hsv.astype(np.float32) + + # 绿 青 + mmax = 105 + mmin = 40 + + alpha = 1 # 越大效果越猛, 误伤越多 + beta = 4 # 越大效果越猛 + gamma = 0.1 # 越小效果越猛 + + mid = mmin + ((mmax - mmin) / 2) + green_weight = np.abs(hsv[:, :, 0] - mid) / ((mmax - mmin)/2) + green_weight = np.clip(green_weight, 0, 1) + # green_weight = np.tanh(green_weight/alpha) + green_weight = green_weight**beta + gamma + green_weight = np.clip(green_weight, 0, 1) + + green_weight = cv2.blur(green_weight, (11, 11)) + hsv[:, :, 2] = hsv[:, :, 2] * green_weight + + + # 紫 洋红 + mmax = 180 + mmin = 130 + + alpha = 1 # 越大效果越猛, 误伤越多 + beta = 8 + # 越大效果越猛 + gamma = -0.5 # 越小效果越猛 + + mid = mmin + ((mmax - mmin) / 2) + green_weight = np.abs(hsv[:, :, 0] - mid) / ((mmax - mmin)/2) + green_weight = np.clip(green_weight, 0, 1) + # green_weight = np.tanh(green_weight/alpha) + green_weight = (green_weight**beta + gamma) * 2 + green_weight = np.clip(green_weight, 0, 1) + + green_weight = cv2.blur(green_weight, (11, 11)) + hsv[:, :, 2] = hsv[:, :, 2] * green_weight + + + hsv = np.clip(hsv, 0, 255) + hsv = hsv.round().astype(np.uint8) + + img = cv2.cvtColor(hsv, cv2.COLOR_HLS2RGB) # RGB U8 + img = np.clip(img, 0, 255) + + + + + + img = np.clip(img, 0, 255).round().astype(np.uint8) + meta_all['img'] = img # RGB U8 + return meta_all + + + +def sky_enhancement(results): + model_path = "sky_seg.pt" + model = torch.load(model_path) + model.cuda() + model.eval() + + for meta_all in tqdm(results): + if float(meta_all['meta']["noise_profile"][0]) >= 0.005: + continue + + ori_img = meta_all['img'].copy().astype(np.float32) # RGB 0-255 U8 + + img = ori_img.copy() + img = cv2.cvtColor(img, cv2.COLOR_RGB2BGR) + + + # 天空分割 + scene_image = img.copy().astype(np.float32) # 0-255, bgr + + + img = img / 255. + lab = cv2.cvtColor(img, cv2.COLOR_BGR2LAB) + # 减少色温 + lab[:,:,1] = lab[:,:,1] - (lab[:,:,2] + 127) * 0.03 + lab[:,:,2] = lab[:,:,2] - (lab[:,:,2] + 127) * 0.1 + # 将图像从LAB空间转换回BGR空间 + img = cv2.cvtColor(lab, cv2.COLOR_LAB2RGB) + img = img * 255 + img = np.clip(img, 0, 255).round().astype(np.float32) + + + + img_mean = 0 + img_std = 255.0 + size = (512, 512) + img_h , img_w = img.shape[:2] + + img = cv2.resize(img, size) + img = (img - img_mean) / img_std + img = np.transpose(img, [2, 0, 1]) + img = np.expand_dims(img, axis=0) + img = torch.from_numpy(img).cuda() + + with torch.no_grad(): + mask = model(img) + + mask = mask.detach().cpu() + mask = mask.permute((0,3,1,2)) + mask = F.interpolate(mask, + size=[img_h , img_w], + mode='bilinear') + mask = mask[0].permute((1,2,0)) + sky_mask = torch.argmax(mask, axis=2).numpy().astype(np.float32) + + if sky_mask.max() < 0.1: + continue + + + # + img = ori_img.copy() # RGB + mask = img[:, :, 2] - np.max(img[:, :, :2], axis=-1) + mask[sky_mask==0]=0 + a = np.sum(mask) + b = np.sum(sky_mask) + ratio_blue = a/b + # print(meta_all['img_path'], "blue ratio", ratio_blue) + + # 非蓝天 + if ratio_blue < 10: + img = ori_img.copy() + mask = np.mean(img[:, :, :2], axis=-1) + mask[sky_mask==0]=0 + a = np.sum(mask) + b = np.sum(sky_mask) + ratio_light = a/b + # print(meta_all['img_path'], "light ratio", ratio_light) + + # 暗天空，压暗 + if ratio_light<50: + img = ori_img.copy() + img = img * 0.88 + img = np.clip(img, 0, 255) # RGB + # 中等亮度，提亮 + elif ratio_light < 200: + img = ori_img.copy() + img = img * 1.1 + img = np.clip(img, 0, 255) # RGB + else: + pass + + hsv = cv2.cvtColor(img.astype(np.uint8), cv2.COLOR_RGB2HSV).astype(np.float32) + hsv[:, :, 1] = hsv[:, :, 1]* 0.4 + hsv = np.clip(hsv, 0, 255).astype(np.uint8) + img = cv2.cvtColor(hsv, cv2.COLOR_HSV2RGB).astype(np.float32) + + # 蓝天 + else: + # LAB + img = ori_img.copy() + img = img / 255. + lab = cv2.cvtColor(img, cv2.COLOR_RGB2LAB) + + # 减少色温 + lab[:,:,1] = lab[:,:,1] - (lab[:,:,2] + 127) * 0.03 + lab[:,:,2] = lab[:,:,2] - (lab[:,:,2] + 127) * 0.1 + + # 将图像从LAB空间转换回BGR空间 + img = cv2.cvtColor(lab, cv2.COLOR_LAB2RGB) + + img = img * 255 + img = np.clip(img, 0, 255).round().astype(np.float32) + + + sky_image = img.copy().astype(np.float32) # 0-255, RGB + sky_image = cv2.cvtColor(sky_image, cv2.COLOR_RGB2BGR) # BGR 0-255 F32 + + sky_mask_ori = sky_mask.copy() + kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (11, 11)) + sky_mask_ori = cv2.erode(sky_mask_ori, kernel) + + sky_mask_ori = sky_mask_ori > 0.9 + + if np.sum(sky_mask_ori) > 0: + h, w = sky_mask.shape + + sky_mask = cv2.resize(sky_mask, None, fx=0.1, fy=0.1, interpolation=cv2.INTER_NEAREST) + kernel = cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (5, 5)) + sky_mask = cv2.dilate(sky_mask, kernel) + sky_mask_blur = cv2.blur(sky_mask, (21, 21)) + sky_mask_blur[sky_mask>0.5] = sky_mask[sky_mask>0.5] + sky_mask = sky_mask_blur + sky_mask = cv2.resize(sky_mask, (w, h), interpolation=cv2.INTER_LINEAR) + sky_mask = np.clip(sky_mask, 0.1, 1) + + sky_area_img = np.zeros_like(sky_image) + sky_area_img[sky_mask_ori] = sky_image[sky_mask_ori] + sky_area_img = cv2.cvtColor(sky_area_img, cv2.COLOR_BGR2GRAY) + + sky_area_img_mean = np.sum(sky_area_img) / np.sum(sky_mask_ori) + if sky_area_img_mean > 20: + res = pyrblend(scene_image, sky_image, sky_mask) + res = np.clip(res, 0, 255) # 0-255, bgr + + res = res.round().astype(np.uint8) + res = cv2.cvtColor(res, cv2.COLOR_BGR2RGB) # RGB 0-255 U8 + meta_all['img'] = res + + + +def post_process2(meta_all): + # PIL + img = meta_all['img'].copy() # RGB U8 + + img = img.astype(np.float32) / 255. + + yuv = cv.cvtColor(img, cv.COLOR_RGB2YUV) + y, u, v = cv.split(yuv) + y = autocontrast_using_pil(y) + yuv = np.stack([y, u, v], axis=-1) + rgb = cv.cvtColor(yuv, cv.COLOR_YUV2RGB) + rgb = np.clip(rgb, 0, 1) + + img = rgb + + img = np.clip(img*255, 0, 255)# .round().astype(np.uint8) # RGB + + + ori_img = meta_all['img'].copy().astype(np.float32) + mask = np.mean(ori_img, axis=-1) / 255. + mask = mask[:, :, np.newaxis] + mask = np.clip(mask - 0.1, 0, 1) + img = img * mask + ori_img * (1-mask) + img = np.clip(img, 0, 255) + + img = img.round().astype(np.uint8) + meta_all['img'] = img + + return meta_all + + +def save_jpg(meta_all): + img = meta_all['img'] # RGB U8 + out_path = os.path.join(output_path, meta_all['img_path'].split("/")[-1].split(".")[0] + ".jpg") + + cv.imwrite(out_path, img[:, :, ::-1], [cv.IMWRITE_JPEG_QUALITY, 100]) + + + +if __name__ == "__main__": + num_worker = 4 + + all_time = time() + + input_path = "/data" + output_path = "/data" + # input_path = "/ssd/ntire24/nightrender/data/data" + # output_path = "/ssd/ntire24/nightrender/data/data" + os.makedirs(output_path, exist_ok=True) + + + # load img + s_time = time() + input_list = sorted(glob(os.path.join(input_path, "*.png")))# [:4] + + if num_worker > 1: + with mp.Pool(num_worker) as pool: + results = list(tqdm(pool.imap(load_img, input_list), total=len(input_list))) + else: + results = [] + for p in tqdm(input_list): + results.append(load_img(p)) + load_time = time()-s_time + print("load_img time is: ", load_time) + + + # preprocess + s_time = time() + iso_list = [50, 125, 320, 640, 800] + lsc_npy = [np.load("./lsc_npy/{}.npy".format(iso)) for iso in iso_list] + + if num_worker > 1: + with mp.Pool(num_worker) as pool: + results = list(tqdm(pool.imap(pre_process, results), total=len(results))) + else: + for r in tqdm(results): + r = pre_process(r) + del lsc_npy + print("pre_process time is: ", time()-s_time) + + + # raw denoise + s_time = time() + raw_denoise(results) + print("raw_denoise time is: ", time()-s_time) + + + # awb + s_time = time() + predict_white_balance(results) + print("predict_white_balance time is: ", time()-s_time) + + + # convert + s_time = time() + color_matrix = [1.06835938, -0.29882812, -0.14257812, -0.43164062, 1.35546875, 0.05078125, -0.1015625, 0.24414062, 0.5859375] + lsc = np.load("lsc.npy") + if num_worker > 1: + with mp.Pool(num_worker) as pool: + results = list(tqdm(pool.imap(convert_to_rgb, results), total=len(results))) + else: + for r in tqdm(results): + r = convert_to_rgb(r) + del lsc + print("convert_to_rgb time is: ", time()-s_time) + + + # NN_enhancement + s_time = time() + nn_enhancement(results) + print("nn_enhancement time is: ", time()-s_time) + + + + # colorfix & sat enhance + s_time = time() + kernel = cv.getStructuringElement(cv.MORPH_RECT, (5, 5)) + if num_worker > 1: + with mp.Pool(num_worker) as pool: + results = list(tqdm(pool.imap(post_process, results), total=len(results))) + else: + for r in tqdm(results): + r = post_process(r) + print("post_process time is: ", time()-s_time) + + + + # sky_enhancement + s_time = time() + sky_enhancement(results) + print("sky_enhancement time is: ", time()-s_time) + + + + # PIL autocontrast + s_time = time() + if num_worker > 1: + with mp.Pool(num_worker) as pool: + results = list(tqdm(pool.imap(post_process2, results), total=len(results))) + else: + for r in tqdm(results): + r = post_process2(r) + print("post_process2 time is: ", time()-s_time) + + + + # save jpg + s_time = time() + if num_worker > 1: + with mp.Pool(num_worker) as pool: + _ = list(tqdm(pool.imap(save_jpg, results), total=len(results))) + else: + for r in tqdm(results): + save_jpg(r) + save_time = time()-s_time + print("save_jpg time is: ", save_time) + + + total_time = time()-all_time + total_time_without_load_save = total_time - load_time - save_time + print("per image inference time (without load and save) is: ", total_time_without_load_save / len(results), "s") \ No newline at end of file diff --git a/MiAlgo/network.py b/MiAlgo/network.py new file mode 100644 index 0000000000000000000000000000000000000000..80ed11bba9f956c72a385794694fda7ba075dcc5 --- /dev/null +++ b/MiAlgo/network.py @@ -0,0 +1,663 @@ +import math +import torch +import torch.optim as optim +import torch.nn as nn +import torch.nn.functional as F +from torch.nn import init, Module +import functools +from torch.optim import lr_scheduler +from collections import OrderedDict +import numpy as np + +''' +# =================================== +# Advanced nn.Sequential +# reform nn.Sequentials and nn.Modules +# to a single nn.Sequential +# =================================== +''' + +def seq(*args): + if len(args) == 1: + args = args[0] + if isinstance(args, nn.Module): + return args + modules = OrderedDict() + if isinstance(args, OrderedDict): + for k, v in args.items(): + modules[k] = seq(v) + return nn.Sequential(modules) + assert isinstance(args, (list, tuple)) + return nn.Sequential(*[seq(i) for i in args]) + +''' +# =================================== +# Useful blocks +# -------------------------------- +# conv (+ normaliation + relu) +# concat +# sum +# resblock (ResBlock) +# resdenseblock (ResidualDenseBlock_5C) +# resinresdenseblock (RRDB) +# =================================== +''' + +# ------------------------------------------------------- +# return nn.Sequantial of (Conv + BN + ReLU) +# ------------------------------------------------------- +def conv(in_channels=64, out_channels=64, kernel_size=3, stride=1, padding=1, + output_padding=0, dilation=1, groups=1, bias=True, + padding_mode='zeros', mode='CBR'): + L = [] + for t in mode: + if t == 'C': + L.append(nn.Conv2d(in_channels=in_channels, + out_channels=out_channels, + kernel_size=kernel_size, + stride=stride, + padding=padding, + dilation=dilation, + groups=groups, + bias=bias, + padding_mode=padding_mode)) + elif t == 'X': + assert in_channels == out_channels + L.append(nn.Conv2d(in_channels=in_channels, + out_channels=out_channels, + kernel_size=kernel_size, + stride=stride, + padding=padding, + dilation=dilation, + groups=in_channels, + bias=bias, + padding_mode=padding_mode)) + elif t == 'T': + L.append(nn.ConvTranspose2d(in_channels=in_channels, + out_channels=out_channels, + kernel_size=kernel_size, + stride=stride, + padding=padding, + output_padding=output_padding, + groups=groups, + bias=bias, + dilation=dilation, + padding_mode=padding_mode)) + elif t == 'B': + L.append(nn.BatchNorm2d(out_channels)) + elif t == 'I': + L.append(nn.InstanceNorm2d(out_channels, affine=True)) + elif t == 'i': + L.append(nn.InstanceNorm2d(out_channels)) + elif t == 'R': + L.append(nn.ReLU(inplace=True)) + elif t == 'r': + L.append(nn.ReLU(inplace=False)) + elif t == 'P': + L.append(nn.PReLU()) + elif t == 'L': + L.append(nn.LeakyReLU(negative_slope=1e-1, inplace=True)) + elif t == 'l': + L.append(nn.LeakyReLU(negative_slope=1e-1, inplace=False)) + elif t == '2': + L.append(nn.PixelShuffle(upscale_factor=2)) + elif t == '3': + L.append(nn.PixelShuffle(upscale_factor=3)) + elif t == '4': + L.append(nn.PixelShuffle(upscale_factor=4)) + elif t == 'U': + L.append(nn.Upsample(scale_factor=2, mode='nearest')) + elif t == 'u': + L.append(nn.Upsample(scale_factor=3, mode='nearest')) + elif t == 'M': + L.append(nn.MaxPool2d(kernel_size=kernel_size, + stride=stride, + padding=0)) + elif t == 'A': + L.append(nn.AvgPool2d(kernel_size=kernel_size, + stride=stride, + padding=0)) + else: + raise NotImplementedError('Undefined type: '.format(t)) + return seq(*L) + +# ------------------------------------------------------- +# Concat the output of a submodule to its input +# ------------------------------------------------------- +class ConcatBlock(nn.Module): + def __init__(self, submodule): + super(ConcatBlock, self).__init__() + + self.sub = submodule + + def forward(self, x): + output = torch.cat((x, self.sub(x)), dim=1) + return output + + def __repr__(self): + return self.sub.__repr__() + '_concat' + +# ------------------------------------------------------- +# Elementwise sum the output of a submodule to its input +# ------------------------------------------------------- +class ShortcutBlock(nn.Module): + def __init__(self, submodule): + super(ShortcutBlock, self).__init__() + + self.sub = submodule + + def forward(self, x): + output = x + self.sub(x) + return output + + def __repr__(self): + tmpstr = 'Identity + \n|' + modstr = self.sub.__repr__().replace('\n', '\n|') + tmpstr = tmpstr + modstr + return tmpstr + +class DWTForward(nn.Module): + def __init__(self): + super(DWTForward, self).__init__() + ll = np.array([[0.5, 0.5], [0.5, 0.5]]) + lh = np.array([[-0.5, -0.5], [0.5, 0.5]]) + hl = np.array([[-0.5, 0.5], [-0.5, 0.5]]) + hh = np.array([[0.5, -0.5], [-0.5, 0.5]]) + filts = np.stack([ll[None,::-1,::-1], lh[None,::-1,::-1], + hl[None,::-1,::-1], hh[None,::-1,::-1]], + axis=0) + self.weight = nn.Parameter( + torch.tensor(filts).to(torch.get_default_dtype()), + requires_grad=False) + def forward(self, x): + C = x.shape[1] + filters = torch.cat([self.weight,] * C, dim=0) + y = F.conv2d(x, filters, groups=C, stride=2) + return y + +class DWTInverse(nn.Module): + def __init__(self): + super(DWTInverse, self).__init__() + ll = np.array([[0.5, 0.5], [0.5, 0.5]]) + lh = np.array([[-0.5, -0.5], [0.5, 0.5]]) + hl = np.array([[-0.5, 0.5], [-0.5, 0.5]]) + hh = np.array([[0.5, -0.5], [-0.5, 0.5]]) + filts = np.stack([ll[None, ::-1, ::-1], lh[None, ::-1, ::-1], + hl[None, ::-1, ::-1], hh[None, ::-1, ::-1]], + axis=0) + self.weight = nn.Parameter( + torch.tensor(filts).to(torch.get_default_dtype()), + requires_grad=False) + + def forward(self, x): + C = int(x.shape[1] / 4) + filters = torch.cat([self.weight, ] * C, dim=0) + y = F.conv_transpose2d(x, filters, groups=C, stride=2) + return y + +# ------------------------------------------------------- +# Channel Attention (CA) Layer +# ------------------------------------------------------- +class CALayer(nn.Module): + def __init__(self, channel=64, reduction=16): + super(CALayer, self).__init__() + + self.avg_pool = nn.AdaptiveAvgPool2d(1) + self.conv_du = nn.Sequential( + nn.Conv2d(channel, channel//reduction, 1, padding=0, bias=True), + nn.ReLU(inplace=True), + nn.Conv2d(channel//reduction, channel, 1, padding=0, bias=True), + nn.Sigmoid() + ) + + def forward(self, x): + y = self.avg_pool(x) + y = self.conv_du(y) + return x * y + +class ChannelPool(nn.Module): + def forward(self, x): + return torch.cat((torch.max(x,1)[0].unsqueeze(1), torch.mean(x,1).unsqueeze(1)), dim=1) + +class spatial_attn_layer(nn.Module): + def __init__(self, kernel_size=3): + super(spatial_attn_layer, self).__init__() + self.compress = ChannelPool() + self.spatial = nn.Conv2d(2, 1, 3, stride=1, padding=1, bias=True) + + def forward(self, x): + # import pdb;pdb.set_trace() + x_compress = self.compress(x) + x_out = self.spatial(x_compress) + scale = torch.sigmoid(x_out) # broadcasting + return x * scale + +# ------------------------------------------------------- +# Content Unrelated Channel Attention (CUCA) Layer +# ------------------------------------------------------- +class CUCALayer(nn.Module): + def __init__(self, channel=64, min=0, max=None): + super(CUCALayer, self).__init__() + + self.attention = nn.Conv2d(channel, channel, 1, padding=0, + groups=channel, bias=False) + self.min, self.max = min, max + nn.init.uniform_(self.attention.weight, 0, 1) + + def forward(self, x): + self.attention.weight.data.clamp_(self.min, self.max) + return self.attention(x) + + +# ------------------------------------------------------- +# Res Block: x + conv(relu(conv(x))) +# ------------------------------------------------------- +class ResBlock(nn.Module): + def __init__(self, in_channels=64, out_channels=64, kernel_size=3, stride=1, + padding=1, bias=True, mode='CRC'): + super(ResBlock, self).__init__() + + assert in_channels == out_channels + if mode[0] in ['R','L']: + mode = mode[0].lower() + mode[1:] + + self.res = conv(in_channels, out_channels, kernel_size, + stride, padding, bias=bias, mode=mode) + + def forward(self, x): + res = self.res(x) + return x + res + +# ------------------------------------------------------- +# Residual Channel Attention Block (RCAB) +# ------------------------------------------------------- +class RCABlock(nn.Module): + def __init__(self, in_channels=64, out_channels=64, kernel_size=3, stride=1, + padding=1, bias=True, mode='CRC', reduction=16): + super(RCABlock, self).__init__() + assert in_channels == out_channels + if mode[0] in ['R','L']: + mode = mode[0].lower() + mode[1:] + + self.res = conv(in_channels, out_channels, kernel_size, + stride, padding, bias=bias, mode=mode) + # self.CA = CALayer(out_channels, reduction) + #self.SA = spatial_attn_layer() ## Spatial Attention + #self.conv1x1 = nn.Conv2d(in_channels*2, in_channels, kernel_size=1) + + def forward(self, x): + res = self.res(x) + #sa_branch = self.SA(res) + # ca_branch = self.CA(res) + #res = torch.cat([sa_branch, ca_branch], dim=1) + #res = self.conv1x1(res) + return res + x + + +# ------------------------------------------------------- +# Residual Channel Attention Group (RG) +# ------------------------------------------------------- +class RCAGroup(nn.Module): + def __init__(self, in_channels=64, out_channels=64, kernel_size=3, stride=1, + padding=1, bias=True, mode='CRC', reduction=16, nb=12, num_attention_block=4, use_attention=True): + super(RCAGroup, self).__init__() + assert in_channels == out_channels + if mode[0] in ['R','L']: + mode = mode[0].lower() + mode[1:] + + RG = [] + for _ in range(num_attention_block): + RG.extend([RCABlock(in_channels, out_channels, kernel_size, stride, padding, + bias, mode, reduction) for _ in range(nb//num_attention_block)]) + if use_attention: + RG.append(AttentionResBlock(in_channels)) + RG.append(conv(out_channels, out_channels, mode='C')) + + # self.rg = ShortcutBlock(nn.Sequential(*RG)) + self.rg = nn.Sequential(*RG) + + + def forward(self, x): + res = self.rg(x) + return res + x + +# ------------------------------------------------------- +# conv + subp + relu +# ------------------------------------------------------- +def upsample_pixelshuffle(in_channels=64, out_channels=3, kernel_size=3, + stride=1, padding=1, bias=True, mode='2R'): + # mode examples: 2, 2R, 2BR, 3, ..., 4BR. + assert len(mode)<4 and mode[0] in ['2', '3', '4'] + up1 = conv(in_channels, out_channels * (int(mode[0]) ** 2), kernel_size, + stride, padding, bias=bias, mode='C'+mode) + return up1 + + +# ------------------------------------------------------- +# nearest_upsample + conv + relu +# ------------------------------------------------------- +def upsample_upconv(in_channels=64, out_channels=3, kernel_size=3, stride=1, + padding=1, bias=True, mode='2R'): + # mode examples: 2, 2R, 2BR, 3, ..., 3BR. + assert len(mode)<4 and mode[0] in ['2', '3'] + if mode[0] == '2': + uc = 'UC' + elif mode[0] == '3': + uc = 'uC' + mode = mode.replace(mode[0], uc) + up1 = conv(in_channels, out_channels, kernel_size, stride, + padding, bias=bias, mode=mode) + return up1 + + +# ------------------------------------------------------- +# convTranspose + relu +# ------------------------------------------------------- +def upsample_convtranspose(in_channels=64, out_channels=3, kernel_size=2, + stride=2, padding=0, bias=True, mode='2R'): + # mode examples: 2, 2R, 2BR, 3, ..., 4BR. + assert len(mode)<4 and mode[0] in ['2', '3', '4'] + kernel_size = int(mode[0]) + stride = int(mode[0]) + mode = mode.replace(mode[0], 'T') + up1 = conv(in_channels, out_channels, kernel_size, stride, + padding, bias=bias, mode=mode) + return up1 + + +''' +# ====================== +# Downsampler +# ====================== +''' + + +# ------------------------------------------------------- +# strideconv + relu +# ------------------------------------------------------- +def downsample_strideconv(in_channels=64, out_channels=64, kernel_size=2, + stride=2, padding=0, bias=True, mode='2R'): + # mode examples: 2, 2R, 2BR, 3, ..., 4BR. + assert len(mode)<4 and mode[0] in ['2', '3', '4'] + kernel_size = int(mode[0]) + stride = int(mode[0]) + mode = mode.replace(mode[0], 'C') + down1 = conv(in_channels, out_channels, kernel_size, stride, + padding, bias=bias, mode=mode) + return down1 + + +# ------------------------------------------------------- +# maxpooling + conv + relu +# ------------------------------------------------------- +def downsample_maxpool(in_channels=64, out_channels=64, kernel_size=3, + stride=1, padding=0, bias=True, mode='2R'): + # mode examples: 2, 2R, 2BR, 3, ..., 3BR. + assert len(mode)<4 and mode[0] in ['2', '3'] + kernel_size_pool = int(mode[0]) + stride_pool = int(mode[0]) + mode = mode.replace(mode[0], 'MC') + pool = conv(kernel_size=kernel_size_pool, stride=stride_pool, mode=mode[0]) + pool_tail = conv(in_channels, out_channels, kernel_size, stride, + padding, bias=bias, mode=mode[1:]) + return sequential(pool, pool_tail) + + +# ------------------------------------------------------- +# averagepooling + conv + relu +# ------------------------------------------------------- +def downsample_avgpool(in_channels=64, out_channels=64, kernel_size=3, + stride=1, padding=1, bias=True, mode='2R'): + # mode examples: 2, 2R, 2BR, 3, ..., 3BR. + assert len(mode)<4 and mode[0] in ['2', '3'] + kernel_size_pool = int(mode[0]) + stride_pool = int(mode[0]) + mode = mode.replace(mode[0], 'AC') + pool = conv(kernel_size=kernel_size_pool, stride=stride_pool, mode=mode[0]) + pool_tail = conv(in_channels, out_channels, kernel_size, stride, + padding, bias=bias, mode=mode[1:]) + return sequential(pool, pool_tail) + + + +class AttentionResBlock(nn.Module): + def __init__(self, dim: int): + super(AttentionResBlock, self).__init__() + self._spatial_attention_conv = nn.Conv2d(2, dim, kernel_size=3, padding=1) + + # Channel attention MLP + self._channel_attention_conv0 = nn.Conv2d(1, dim, kernel_size=1, padding=0) + self._channel_attention_conv1 = nn.Conv2d(dim, dim, kernel_size=1, padding=0) + + self._out_conv = nn.Conv2d(2 * dim, dim, kernel_size=1, padding=0) + + def forward(self, x: torch.Tensor): + # Spatial attention + mean = torch.mean(x, dim=1, keepdim=True) # Mean/Max on C axis + max, _ = torch.max(x, dim=1, keepdim=True) + spatial_attention = torch.cat([mean, max], dim=1) # [B, 2, H, W] + spatial_attention = self._spatial_attention_conv(spatial_attention) + spatial_attention = torch.sigmoid(spatial_attention) * x + + channel_attention = torch.relu(self._channel_attention_conv0(mean)) + channel_attention = self._channel_attention_conv1(channel_attention) + channel_attention = torch.sigmoid(channel_attention) * x + + attention = torch.cat([spatial_attention, channel_attention], dim=1) # [B, 2*dim, H, W] + attention = self._out_conv(attention) + return x + attention + + +class MWRCANv2(nn.Module): + def __init__(self): + super(MWRCANv2, self).__init__() + c1 = 64 + c2 = 96 + c3 = 128 + n_b = 16 + + self.head = seq( + nn.PixelUnshuffle(2), + DWTForward() + ) + + self.down1 = seq( + nn.Conv2d(48, c1, 3, 1, 1), + nn.PReLU(), + RCAGroup(in_channels=c1, out_channels=c1, nb=n_b, num_attention_block=4) + ) + + self.down2 = seq( + DWTForward(), + nn.Conv2d(c1 * 4, c2, 3, 1, 1), + nn.PReLU(), + RCAGroup(in_channels=c2, out_channels=c2, nb=n_b, num_attention_block=4) + ) + + self.down3 = seq( + DWTForward(), + nn.Conv2d(c2 * 4, c3, 3, 1, 1), + nn.PReLU() + ) + + self.middle = seq( + RCAGroup(in_channels=c3, out_channels=c3, nb=n_b, num_attention_block=4), + RCAGroup(in_channels=c3, out_channels=c3, nb=n_b, num_attention_block=4) + ) + + self.up1 = seq( + nn.Conv2d(c3, c2 * 4, 3, 1, 1), + nn.PReLU(), + DWTInverse() + ) + + self.up2 = seq( + RCAGroup(in_channels=c2, out_channels=c2, nb=n_b, num_attention_block=4), + nn.Conv2d(c2, c1 * 4, 3, 1, 1), + nn.PReLU(), + DWTInverse() + ) + + self.up3 = seq( + RCAGroup(in_channels=c1, out_channels=c1, nb=n_b, num_attention_block=4), + nn.Conv2d(c1, 48, 3, 1, 1) + ) + + self.tail = seq( + DWTInverse(), + nn.PixelShuffle(2) + ) + + def forward(self, x, c=None): + c1 = self.head(x) + c2 = self.down1(c1) + c3 = self.down2(c2) + c4 = self.down3(c3) + m = self.middle(c4) + c5 = self.up1(m) + c3 + c6 = self.up2(c5) + c2 + c7 = self.up3(c6) + c1 + out = self.tail(c7) + + return out + + + +class MWRCANv3(nn.Module): + def __init__(self): + super(MWRCANv3, self).__init__() + c1 = 64 + c2 = 96 + c3 = 128 + n_b = 16 + + self.head = seq( + DWTForward() + ) + + self.down1 = seq( + nn.Conv2d(12, c1, 3, 1, 1), + nn.PReLU(), + RCAGroup(in_channels=c1, out_channels=c1, nb=n_b) + ) + + self.down2 = seq( + DWTForward(), + nn.Conv2d(c1 * 4, c2, 3, 1, 1), + nn.PReLU(), + RCAGroup(in_channels=c2, out_channels=c2, nb=n_b) + ) + + self.down3 = seq( + DWTForward(), + nn.Conv2d(c2 * 4, c3, 3, 1, 1), + nn.PReLU() + ) + + self.middle = seq( + RCAGroup(in_channels=c3, out_channels=c3, nb=n_b), + RCAGroup(in_channels=c3, out_channels=c3, nb=n_b) + ) + + self.up1 = seq( + nn.Conv2d(c3, c2 * 4, 3, 1, 1), + nn.PReLU(), + DWTInverse() + ) + + self.up2 = seq( + RCAGroup(in_channels=c2, out_channels=c2, nb=n_b), + nn.Conv2d(c2, c1 * 4, 3, 1, 1), + nn.PReLU(), + DWTInverse() + ) + + self.up3 = seq( + RCAGroup(in_channels=c1, out_channels=c1, nb=n_b), + nn.Conv2d(c1, 12, 3, 1, 1) + ) + + self.tail = seq( + DWTInverse() + ) + + def forward(self, x, c=None): + c1 = self.head(x) + c2 = self.down1(c1) + c3 = self.down2(c2) + c4 = self.down3(c3) + m = self.middle(c4) + c5 = self.up1(m) + c3 + c6 = self.up2(c5) + c2 + c7 = self.up3(c6) + c1 + out = self.tail(c7) + + return out + + +class MWRCANv4(nn.Module): + def __init__(self, c1 = 64, c2 = 96, c3 = 128, n_b = 16): + super(MWRCANv4, self).__init__() + + self.head = seq( + DWTForward() + ) + + self.down1 = seq( + nn.Conv2d(12, c1, 3, 1, 1), + nn.PReLU(), + RCAGroup(in_channels=c1, out_channels=c1, nb=n_b, use_attention=False) + ) + + self.down2 = seq( + DWTForward(), + nn.Conv2d(c1 * 4, c2, 3, 1, 1), + nn.PReLU(), + RCAGroup(in_channels=c2, out_channels=c2, nb=n_b, use_attention=False) + ) + + self.down3 = seq( + DWTForward(), + nn.Conv2d(c2 * 4, c3, 3, 1, 1), + nn.PReLU() + ) + + self.middle = seq( + RCAGroup(in_channels=c3, out_channels=c3, nb=n_b, use_attention=False), + RCAGroup(in_channels=c3, out_channels=c3, nb=n_b, use_attention=False) + ) + + self.up1 = seq( + nn.Conv2d(c3, c2 * 4, 3, 1, 1), + nn.PReLU(), + DWTInverse() + ) + + self.up2 = seq( + RCAGroup(in_channels=c2, out_channels=c2, nb=n_b, use_attention=False), + nn.Conv2d(c2, c1 * 4, 3, 1, 1), + nn.PReLU(), + DWTInverse() + ) + + self.up3 = seq( + RCAGroup(in_channels=c1, out_channels=c1, nb=n_b, use_attention=False), + nn.Conv2d(c1, 12, 3, 1, 1) + ) + + self.tail = seq( + DWTInverse() + ) + + def forward(self, x, c=None): + c1 = self.head(x) + c2 = self.down1(c1) + c3 = self.down2(c2) + c4 = self.down3(c3) + m = self.middle(c4) + c5 = self.up1(m) + c3 + c6 = self.up2(c5) + c2 + c7 = self.up3(c6) + out = self.tail(c7) + + return out \ No newline at end of file diff --git a/MiAlgo/network_raw_denoise.py b/MiAlgo/network_raw_denoise.py new file mode 100644 index 0000000000000000000000000000000000000000..328559cdb8659a1a17a7d5064d34c284d4b2f875 --- /dev/null +++ b/MiAlgo/network_raw_denoise.py @@ -0,0 +1,141 @@ +''' +def upsample_and_sum(x1, x2,output_channels,in_channels): + pool_size = 2 + deconv_filter = tf.Variable(tf.truncated_normal([pool_size, pool_size, output_channels, in_channels], stddev=0.02)) + deconv = tf.nn.conv2d_transpose(x1, deconv_filter, tf.shape(x2), strides=[1, pool_size, pool_size, 1]) + + deconv_output = tf.add(deconv,x2) + return deconv_output + + +def sc_net_1f(input): + # scratch capture single frame denoise network + # unet_2down_res_relu_64c5 + + with slim.arg_scope([slim.conv2d], weights_initializer=slim.variance_scaling_initializer(), + weights_regularizer=slim.l1_regularizer(0.0001),biases_initializer = None): + + conv1 = slim.conv2d(input, 64, [3, 3], rate=1, activation_fn=relu, scope='conv1_1') + res_conv1 = slim.conv2d(conv1, 64, [3, 3], rate=1, activation_fn=relu, scope='res_conv1_1') + res_conv1 = slim.conv2d(res_conv1, 64, [3, 3], rate=1, activation_fn=relu, scope='res_conv1_2') + res_block1 = conv1 + res_conv1 + + + pool2 = slim.avg_pool2d(res_block1,[2,2],padding='SAME') + res_conv2 = slim.conv2d(pool2, 64, [3, 3], rate=1, activation_fn=relu, scope='res_conv2_1') + res_conv2 = slim.conv2d(res_conv2, 64, [3, 3], rate=1, activation_fn=relu, scope='res_conv2_2') + res_block2 = pool2 + res_conv2 + + pool3 = slim.avg_pool2d(res_block2,[2,2],padding='SAME') + res_conv3 = slim.conv2d(pool3, 64, [3, 3], rate=1, activation_fn=relu, scope='res_conv3_1') + res_conv3 = slim.conv2d(res_conv3, 64, [3, 3], rate=1, activation_fn=relu, scope='res_conv3_2') + res_block3 = pool3 + res_conv3 + + deconv1 = upsample_and_sum(res_block3, res_block2, 64, 64) + + conv4 = slim.conv2d(deconv1, 64, [3, 3], rate=1, stride=1, activation_fn=relu, scope='conv4_1') + res_conv4 = slim.conv2d(conv4, 64, [3, 3], rate=1, activation_fn=relu, scope='res_conv4_1') + res_conv4 = slim.conv2d(res_conv4, 64, [3, 3], rate=1, activation_fn=relu, scope='res_conv4_2') + res_block4 = conv4 + res_conv4 + + deconv2 = upsample_and_sum(res_block4, res_block1, 64, 64) + + conv5 = slim.conv2d(deconv2, 64, [3, 3], rate=1, stride=1, activation_fn=relu, scope='conv5_1') + res_conv5 = slim.conv2d(conv5, 64, [3, 3], rate=1, activation_fn=relu, scope='res_conv5_1') + res_conv5 = slim.conv2d(res_conv5, 64, [3, 3], rate=1, activation_fn=relu, scope='res_conv5_2') + res_block5 = conv5 + res_conv5 + + conv6 = slim.conv2d(res_block5, 64, [3, 3], rate=1, stride=1, activation_fn=relu, scope='conv6_1') + conv7 = slim.conv2d(conv6, 4, [3, 3], rate=1, stride=1, activation_fn=None, scope='conv7_1') + + out = conv7 + + return out +''' + +import numpy as np + +import torch +import torch.nn as nn + + +class sc_net_1f(nn.Module): + def __init__(self): + super().__init__() + + self.conv1_1 = nn.Conv2d(in_channels=4, out_channels=64, kernel_size=3, padding=1, stride=1, bias=False) + self.res_conv1_1 = nn.Conv2d(in_channels=64, out_channels=64, kernel_size=3, padding=1, stride=1, bias=False) + self.res_conv1_2 = nn.Conv2d(in_channels=64, out_channels=64, kernel_size=3, padding=1, stride=1, bias=False) + + self.pool2 = nn.AvgPool2d(2) + self.res_conv2_1 = nn.Conv2d(in_channels=64, out_channels=64, kernel_size=3, padding=1, stride=1, bias=False) + self.res_conv2_2 = nn.Conv2d(in_channels=64, out_channels=64, kernel_size=3, padding=1, stride=1, bias=False) + + self.pool3 = nn.AvgPool2d(2) + self.res_conv3_1 = nn.Conv2d(in_channels=64, out_channels=64, kernel_size=3, padding=1, stride=1, bias=False) + self.res_conv3_2 = nn.Conv2d(in_channels=64, out_channels=64, kernel_size=3, padding=1, stride=1, bias=False) + + self.deconv1 = nn.ConvTranspose2d(in_channels=64, out_channels=64, kernel_size=2, padding=0, stride=2, bias=False) + + self.conv4_1 = nn.Conv2d(in_channels=64, out_channels=64, kernel_size=3, padding=1, stride=1, bias=False) + self.res_conv4_1 = nn.Conv2d(in_channels=64, out_channels=64, kernel_size=3, padding=1, stride=1, bias=False) + self.res_conv4_2 = nn.Conv2d(in_channels=64, out_channels=64, kernel_size=3, padding=1, stride=1, bias=False) + + self.deconv2 = nn.ConvTranspose2d(in_channels=64, out_channels=64, kernel_size=2, padding=0, stride=2, bias=False) + + self.conv5_1 = nn.Conv2d(in_channels=64, out_channels=64, kernel_size=3, padding=1, stride=1, bias=False) + self.res_conv5_1 = nn.Conv2d(in_channels=64, out_channels=64, kernel_size=3, padding=1, stride=1, bias=False) + self.res_conv5_2 = nn.Conv2d(in_channels=64, out_channels=64, kernel_size=3, padding=1, stride=1, bias=False) + + self.conv6_1 = nn.Conv2d(in_channels=64, out_channels=64, kernel_size=3, padding=1, stride=1, bias=False) + self.conv7_1 = nn.Conv2d(in_channels=64, out_channels=4, kernel_size=3, padding=1, stride=1, bias=False) + + self.relu = nn.ReLU(inplace=True) + + + def upsample_and_sum(x1, x2,output_channels,in_channels): + pool_size = 2 + deconv_filter = tf.Variable(tf.truncated_normal([pool_size, pool_size, output_channels, in_channels], stddev=0.02)) + deconv = tf.nn.conv2d_transpose(x1, deconv_filter, tf.shape(x2), strides=[1, pool_size, pool_size, 1]) + + deconv_output = tf.add(deconv,x2) + return deconv_output + + def forward(self, inp): + conv1 = self.relu(self.conv1_1(inp)) + res_conv1 = self.relu(self.res_conv1_1(conv1)) + res_conv1 = self.relu(self.res_conv1_2(res_conv1)) + res_block1 = conv1 + res_conv1 + + pool2 = self.pool2(res_block1) + res_conv2 = self.relu(self.res_conv2_1(pool2)) + res_conv2 = self.relu(self.res_conv2_2(res_conv2)) + res_block2 = pool2 + res_conv2 + + pool3 = self.pool3(res_block2) + res_conv3 = self.relu(self.res_conv3_1(pool3)) + res_conv3 = self.relu(self.res_conv3_2(res_conv3)) + res_block3 = pool3 + res_conv3 + + deconv1 = self.deconv1(res_block3) + res_block2 + + conv4 = self.relu(self.conv4_1(deconv1)) + res_conv4 = self.relu(self.res_conv4_1(conv4)) + res_conv4 = self.relu(self.res_conv4_2(res_conv4)) + res_block4 = conv4 + res_conv4 + + deconv2 = self.deconv2(res_block4) + res_block1 + + conv5 = self.relu(self.conv5_1(deconv2)) + res_conv5 = self.relu(self.res_conv5_1(conv5)) + res_conv5 = self.relu(self.res_conv5_2(res_conv5)) + res_block5 = conv5 + res_conv5 + + conv6 = self.relu(self.conv6_1(res_block5)) + conv7 = self.conv7_1(conv6) + + out = conv7 + + return out + + diff --git a/MiAlgo/run.sh b/MiAlgo/run.sh new file mode 100644 index 0000000000000000000000000000000000000000..ada35f468484df348b2278cecf3cf6379e132251 --- /dev/null +++ b/MiAlgo/run.sh @@ -0,0 +1,7 @@ +#!/bin/bash +export PATH=/opt/conda/bin:/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin +_=/usr/bin/env + +python main_test.py + + diff --git a/MiAlgo/sky_seg.pt b/MiAlgo/sky_seg.pt new file mode 100644 index 0000000000000000000000000000000000000000..8cb96eca4bd1f3022912137998895323207dbabe --- /dev/null +++ b/MiAlgo/sky_seg.pt @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:75cbb55110ca846a55eadc8ed3f122081ff71de069bbe8cf34b61d755feb406e +size 27338527 diff --git a/MiAlgo/tone_curve.mat b/MiAlgo/tone_curve.mat new file mode 100644 index 0000000000000000000000000000000000000000..af56812d14be86467529eccc094f988dd926ed0c Binary files /dev/null and b/MiAlgo/tone_curve.mat differ diff --git a/MiAlgo/trained_models/fc4_cwp/fold_0/metrics.csv b/MiAlgo/trained_models/fc4_cwp/fold_0/metrics.csv new file mode 100644 index 0000000000000000000000000000000000000000..fa399050f3edafb5d3ebdc4baf55454276b2ddf7 --- /dev/null +++ b/MiAlgo/trained_models/fc4_cwp/fold_0/metrics.csv @@ -0,0 +1,2001 @@ +train_loss,val_loss,best_mean,best_median,best_trimean,best_bst25,best_wst25,best_wst5,mean,median,trimean,bst25,wst25,wst5 +13.005475762808858,8.579540528317608,8.579540528317608,6.186561107635498,7.0686503648757935,3.5271973736742708,17.53066247701645,19.366010284423826,8.579540528317608,6.186561107635498,7.0686503648757935,3.5271973736742708,17.53066247701645,19.366010284423826 +10.88077814880965,0,8.579540528317608,6.186561107635498,7.0686503648757935,3.5271973736742708,17.53066247701645,19.366010284423826,8.579540528317608,6.186561107635498,7.0686503648757935,3.5271973736742708,17.53066247701645,19.366010284423826 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a/MiAlgo/trained_models/fc4_cwp/fold_0/model.pth b/MiAlgo/trained_models/fc4_cwp/fold_0/model.pth new file mode 100644 index 0000000000000000000000000000000000000000..1cb92a7408c8a181a4af7a811972725aaf5c09af --- /dev/null +++ b/MiAlgo/trained_models/fc4_cwp/fold_0/model.pth @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:c46f1185706d3ee09635ab17ce9571bdba70dbddc912ce4bb41a89d52b429870 +size 6839487 diff --git a/MiAlgo/trained_models/fc4_cwp/fold_0/network.txt b/MiAlgo/trained_models/fc4_cwp/fold_0/network.txt new file mode 100644 index 0000000000000000000000000000000000000000..1ed85bb2dd441f8f2138348e3d7649534b5504c9 --- /dev/null +++ b/MiAlgo/trained_models/fc4_cwp/fold_0/network.txt @@ -0,0 +1,73 @@ +FC4( + (backbone): Sequential( + (0): Conv2d(3, 64, kernel_size=(3, 3), stride=(2, 2)) + (1): ReLU(inplace=True) + (2): MaxPool2d(kernel_size=3, stride=2, padding=0, dilation=1, ceil_mode=True) + (3): Fire( + (squeeze): Conv2d(64, 16, kernel_size=(1, 1), stride=(1, 1)) + (squeeze_activation): ReLU(inplace=True) + (expand1x1): Conv2d(16, 64, kernel_size=(1, 1), stride=(1, 1)) + (expand1x1_activation): ReLU(inplace=True) + (expand3x3): Conv2d(16, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (expand3x3_activation): ReLU(inplace=True) + ) + (4): Fire( + (squeeze): Conv2d(128, 16, kernel_size=(1, 1), stride=(1, 1)) + (squeeze_activation): ReLU(inplace=True) + (expand1x1): Conv2d(16, 64, kernel_size=(1, 1), stride=(1, 1)) + (expand1x1_activation): ReLU(inplace=True) + (expand3x3): Conv2d(16, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (expand3x3_activation): ReLU(inplace=True) + ) + (5): MaxPool2d(kernel_size=3, stride=2, padding=0, dilation=1, ceil_mode=True) + (6): Fire( + (squeeze): Conv2d(128, 32, kernel_size=(1, 1), stride=(1, 1)) + (squeeze_activation): ReLU(inplace=True) + (expand1x1): Conv2d(32, 128, kernel_size=(1, 1), stride=(1, 1)) + (expand1x1_activation): ReLU(inplace=True) + (expand3x3): Conv2d(32, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (expand3x3_activation): ReLU(inplace=True) + ) + (7): Fire( + (squeeze): Conv2d(256, 32, kernel_size=(1, 1), stride=(1, 1)) + (squeeze_activation): ReLU(inplace=True) + (expand1x1): Conv2d(32, 128, kernel_size=(1, 1), stride=(1, 1)) + (expand1x1_activation): ReLU(inplace=True) + (expand3x3): Conv2d(32, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (expand3x3_activation): ReLU(inplace=True) + ) + (8): MaxPool2d(kernel_size=3, stride=2, padding=0, dilation=1, ceil_mode=True) + (9): Fire( + (squeeze): Conv2d(256, 48, kernel_size=(1, 1), stride=(1, 1)) + (squeeze_activation): ReLU(inplace=True) + (expand1x1): Conv2d(48, 192, kernel_size=(1, 1), stride=(1, 1)) + (expand1x1_activation): ReLU(inplace=True) + (expand3x3): Conv2d(48, 192, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (expand3x3_activation): ReLU(inplace=True) + ) + (10): Fire( + (squeeze): Conv2d(384, 48, kernel_size=(1, 1), stride=(1, 1)) + (squeeze_activation): ReLU(inplace=True) + (expand1x1): Conv2d(48, 192, kernel_size=(1, 1), stride=(1, 1)) + (expand1x1_activation): ReLU(inplace=True) + (expand3x3): Conv2d(48, 192, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (expand3x3_activation): ReLU(inplace=True) + ) + (11): Fire( + (squeeze): Conv2d(384, 64, kernel_size=(1, 1), stride=(1, 1)) + (squeeze_activation): ReLU(inplace=True) + (expand1x1): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1)) + (expand1x1_activation): ReLU(inplace=True) + (expand3x3): Conv2d(64, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (expand3x3_activation): ReLU(inplace=True) + ) + ) + (final_convs): Sequential( + (0): MaxPool2d(kernel_size=2, stride=1, padding=0, dilation=1, ceil_mode=True) + (1): Conv2d(512, 64, kernel_size=(6, 6), stride=(1, 1), padding=(3, 3)) + (2): ReLU(inplace=True) + (3): Dropout(p=0.5, inplace=False) + (4): Conv2d(64, 4, kernel_size=(1, 1), stride=(1, 1)) + (5): ReLU(inplace=True) + ) +) \ No newline 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b/MiAlgo/trained_models/fc4_cwp/fold_1/network.txt new file mode 100644 index 0000000000000000000000000000000000000000..1ed85bb2dd441f8f2138348e3d7649534b5504c9 --- /dev/null +++ b/MiAlgo/trained_models/fc4_cwp/fold_1/network.txt @@ -0,0 +1,73 @@ +FC4( + (backbone): Sequential( + (0): Conv2d(3, 64, kernel_size=(3, 3), stride=(2, 2)) + (1): ReLU(inplace=True) + (2): MaxPool2d(kernel_size=3, stride=2, padding=0, dilation=1, ceil_mode=True) + (3): Fire( + (squeeze): Conv2d(64, 16, kernel_size=(1, 1), stride=(1, 1)) + (squeeze_activation): ReLU(inplace=True) + (expand1x1): Conv2d(16, 64, kernel_size=(1, 1), stride=(1, 1)) + (expand1x1_activation): ReLU(inplace=True) + (expand3x3): Conv2d(16, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (expand3x3_activation): ReLU(inplace=True) + ) + (4): Fire( + (squeeze): Conv2d(128, 16, kernel_size=(1, 1), stride=(1, 1)) + (squeeze_activation): ReLU(inplace=True) + (expand1x1): Conv2d(16, 64, kernel_size=(1, 1), stride=(1, 1)) + (expand1x1_activation): ReLU(inplace=True) + (expand3x3): Conv2d(16, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (expand3x3_activation): ReLU(inplace=True) + ) + (5): MaxPool2d(kernel_size=3, stride=2, padding=0, dilation=1, ceil_mode=True) + (6): Fire( + (squeeze): Conv2d(128, 32, kernel_size=(1, 1), stride=(1, 1)) + (squeeze_activation): ReLU(inplace=True) + (expand1x1): Conv2d(32, 128, kernel_size=(1, 1), stride=(1, 1)) + (expand1x1_activation): ReLU(inplace=True) + (expand3x3): Conv2d(32, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (expand3x3_activation): ReLU(inplace=True) + ) + (7): Fire( + (squeeze): Conv2d(256, 32, kernel_size=(1, 1), stride=(1, 1)) + (squeeze_activation): ReLU(inplace=True) + (expand1x1): Conv2d(32, 128, kernel_size=(1, 1), stride=(1, 1)) + (expand1x1_activation): ReLU(inplace=True) + (expand3x3): Conv2d(32, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (expand3x3_activation): ReLU(inplace=True) + ) + (8): MaxPool2d(kernel_size=3, stride=2, padding=0, dilation=1, ceil_mode=True) + (9): Fire( + (squeeze): Conv2d(256, 48, kernel_size=(1, 1), stride=(1, 1)) + (squeeze_activation): ReLU(inplace=True) + (expand1x1): Conv2d(48, 192, kernel_size=(1, 1), stride=(1, 1)) + (expand1x1_activation): ReLU(inplace=True) + (expand3x3): Conv2d(48, 192, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (expand3x3_activation): ReLU(inplace=True) + ) + (10): Fire( + (squeeze): Conv2d(384, 48, kernel_size=(1, 1), stride=(1, 1)) + (squeeze_activation): ReLU(inplace=True) + (expand1x1): Conv2d(48, 192, kernel_size=(1, 1), stride=(1, 1)) + (expand1x1_activation): ReLU(inplace=True) + (expand3x3): Conv2d(48, 192, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (expand3x3_activation): ReLU(inplace=True) + ) + (11): Fire( + (squeeze): Conv2d(384, 64, kernel_size=(1, 1), stride=(1, 1)) + (squeeze_activation): ReLU(inplace=True) + (expand1x1): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1)) + (expand1x1_activation): ReLU(inplace=True) + (expand3x3): Conv2d(64, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (expand3x3_activation): ReLU(inplace=True) + ) + ) + (final_convs): Sequential( + (0): MaxPool2d(kernel_size=2, stride=1, padding=0, dilation=1, ceil_mode=True) + (1): Conv2d(512, 64, kernel_size=(6, 6), stride=(1, 1), padding=(3, 3)) + (2): ReLU(inplace=True) + (3): Dropout(p=0.5, inplace=False) + (4): Conv2d(64, 4, kernel_size=(1, 1), stride=(1, 1)) + (5): ReLU(inplace=True) + ) +) \ No newline at end of file diff --git a/MiAlgo/trained_models/fc4_cwp/fold_2/metrics.csv b/MiAlgo/trained_models/fc4_cwp/fold_2/metrics.csv new file mode 100644 index 0000000000000000000000000000000000000000..94ef0ace09a3a7d41084172b92b5a7dba373f38a --- /dev/null +++ b/MiAlgo/trained_models/fc4_cwp/fold_2/metrics.csv @@ -0,0 +1,2001 @@ +train_loss,val_loss,best_mean,best_median,best_trimean,best_bst25,best_wst25,best_wst5,mean,median,trimean,bst25,wst25,wst5 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b/MiAlgo/trained_models/fc4_cwp/fold_2/model.pth @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:7ef31a3ea6892f34fc30d6e99ec8f1092aed4cbd8f88ace8b3bdd23e9e0c7827 +size 6839487 diff --git a/MiAlgo/trained_models/fc4_cwp/fold_2/network.txt b/MiAlgo/trained_models/fc4_cwp/fold_2/network.txt new file mode 100644 index 0000000000000000000000000000000000000000..1ed85bb2dd441f8f2138348e3d7649534b5504c9 --- /dev/null +++ b/MiAlgo/trained_models/fc4_cwp/fold_2/network.txt @@ -0,0 +1,73 @@ +FC4( + (backbone): Sequential( + (0): Conv2d(3, 64, kernel_size=(3, 3), stride=(2, 2)) + (1): ReLU(inplace=True) + (2): MaxPool2d(kernel_size=3, stride=2, padding=0, dilation=1, ceil_mode=True) + (3): Fire( + (squeeze): Conv2d(64, 16, kernel_size=(1, 1), stride=(1, 1)) + (squeeze_activation): ReLU(inplace=True) + (expand1x1): Conv2d(16, 64, kernel_size=(1, 1), stride=(1, 1)) + (expand1x1_activation): ReLU(inplace=True) + (expand3x3): Conv2d(16, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (expand3x3_activation): ReLU(inplace=True) + ) + (4): Fire( + (squeeze): Conv2d(128, 16, kernel_size=(1, 1), stride=(1, 1)) + (squeeze_activation): ReLU(inplace=True) + (expand1x1): Conv2d(16, 64, kernel_size=(1, 1), stride=(1, 1)) + (expand1x1_activation): ReLU(inplace=True) + (expand3x3): Conv2d(16, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (expand3x3_activation): ReLU(inplace=True) + ) + (5): MaxPool2d(kernel_size=3, stride=2, padding=0, dilation=1, ceil_mode=True) + (6): Fire( + (squeeze): Conv2d(128, 32, kernel_size=(1, 1), stride=(1, 1)) + (squeeze_activation): ReLU(inplace=True) + (expand1x1): Conv2d(32, 128, kernel_size=(1, 1), stride=(1, 1)) + (expand1x1_activation): ReLU(inplace=True) + (expand3x3): Conv2d(32, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (expand3x3_activation): ReLU(inplace=True) + ) + (7): Fire( + (squeeze): Conv2d(256, 32, kernel_size=(1, 1), stride=(1, 1)) + (squeeze_activation): ReLU(inplace=True) + (expand1x1): Conv2d(32, 128, kernel_size=(1, 1), stride=(1, 1)) + (expand1x1_activation): ReLU(inplace=True) + (expand3x3): Conv2d(32, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (expand3x3_activation): ReLU(inplace=True) + ) + (8): MaxPool2d(kernel_size=3, stride=2, padding=0, dilation=1, ceil_mode=True) + (9): Fire( + (squeeze): Conv2d(256, 48, kernel_size=(1, 1), stride=(1, 1)) + (squeeze_activation): ReLU(inplace=True) + (expand1x1): Conv2d(48, 192, kernel_size=(1, 1), stride=(1, 1)) + (expand1x1_activation): ReLU(inplace=True) + (expand3x3): Conv2d(48, 192, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (expand3x3_activation): ReLU(inplace=True) + ) + (10): Fire( + (squeeze): Conv2d(384, 48, kernel_size=(1, 1), stride=(1, 1)) + (squeeze_activation): ReLU(inplace=True) + (expand1x1): Conv2d(48, 192, kernel_size=(1, 1), stride=(1, 1)) + (expand1x1_activation): ReLU(inplace=True) + (expand3x3): Conv2d(48, 192, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (expand3x3_activation): ReLU(inplace=True) + ) + (11): Fire( + (squeeze): Conv2d(384, 64, kernel_size=(1, 1), stride=(1, 1)) + (squeeze_activation): ReLU(inplace=True) + (expand1x1): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1)) + (expand1x1_activation): ReLU(inplace=True) + (expand3x3): Conv2d(64, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (expand3x3_activation): ReLU(inplace=True) + ) + ) + (final_convs): Sequential( + (0): MaxPool2d(kernel_size=2, stride=1, padding=0, dilation=1, ceil_mode=True) + (1): Conv2d(512, 64, kernel_size=(6, 6), stride=(1, 1), padding=(3, 3)) + (2): ReLU(inplace=True) + (3): Dropout(p=0.5, inplace=False) + (4): Conv2d(64, 4, kernel_size=(1, 1), stride=(1, 1)) + (5): ReLU(inplace=True) + ) +) \ No newline at end of file diff --git a/MiAlgo/trained_models/fc4_sum/fold_0/metrics.csv b/MiAlgo/trained_models/fc4_sum/fold_0/metrics.csv new file mode 100644 index 0000000000000000000000000000000000000000..ef14d54d5d8e625d8ccfb5598219e0700396d8ba 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diff --git a/MiAlgo/trained_models/fc4_sum/fold_0/model.pth b/MiAlgo/trained_models/fc4_sum/fold_0/model.pth new file mode 100644 index 0000000000000000000000000000000000000000..66a3be1989f89eba194b9301709317598574aa55 --- /dev/null +++ b/MiAlgo/trained_models/fc4_sum/fold_0/model.pth @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:74cf2b427f8eebbb457add1a52908030d7856e5e186b6b591da429ee20b59a26 +size 6839233 diff --git a/MiAlgo/trained_models/fc4_sum/fold_0/network.txt b/MiAlgo/trained_models/fc4_sum/fold_0/network.txt new file mode 100644 index 0000000000000000000000000000000000000000..5d712dd9a675ffe55127e74cd4f0a678c7862c1e --- /dev/null +++ b/MiAlgo/trained_models/fc4_sum/fold_0/network.txt @@ -0,0 +1,73 @@ +FC4( + (backbone): Sequential( + (0): Conv2d(3, 64, kernel_size=(3, 3), stride=(2, 2)) + (1): ReLU(inplace=True) + (2): MaxPool2d(kernel_size=3, stride=2, padding=0, dilation=1, ceil_mode=True) + (3): Fire( + (squeeze): Conv2d(64, 16, kernel_size=(1, 1), stride=(1, 1)) + (squeeze_activation): ReLU(inplace=True) + (expand1x1): Conv2d(16, 64, kernel_size=(1, 1), stride=(1, 1)) + (expand1x1_activation): ReLU(inplace=True) + (expand3x3): Conv2d(16, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (expand3x3_activation): ReLU(inplace=True) + ) + (4): Fire( + (squeeze): Conv2d(128, 16, kernel_size=(1, 1), stride=(1, 1)) + (squeeze_activation): ReLU(inplace=True) + (expand1x1): Conv2d(16, 64, kernel_size=(1, 1), stride=(1, 1)) + (expand1x1_activation): ReLU(inplace=True) + (expand3x3): Conv2d(16, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (expand3x3_activation): ReLU(inplace=True) + ) + (5): MaxPool2d(kernel_size=3, stride=2, padding=0, dilation=1, ceil_mode=True) + (6): Fire( + (squeeze): Conv2d(128, 32, kernel_size=(1, 1), stride=(1, 1)) + (squeeze_activation): ReLU(inplace=True) + (expand1x1): Conv2d(32, 128, kernel_size=(1, 1), stride=(1, 1)) + (expand1x1_activation): ReLU(inplace=True) + (expand3x3): Conv2d(32, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (expand3x3_activation): ReLU(inplace=True) + ) + (7): Fire( + (squeeze): Conv2d(256, 32, kernel_size=(1, 1), stride=(1, 1)) + (squeeze_activation): ReLU(inplace=True) + (expand1x1): Conv2d(32, 128, kernel_size=(1, 1), stride=(1, 1)) + (expand1x1_activation): ReLU(inplace=True) + (expand3x3): Conv2d(32, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (expand3x3_activation): ReLU(inplace=True) + ) + (8): MaxPool2d(kernel_size=3, stride=2, padding=0, dilation=1, ceil_mode=True) + (9): Fire( + (squeeze): Conv2d(256, 48, kernel_size=(1, 1), stride=(1, 1)) + (squeeze_activation): ReLU(inplace=True) + (expand1x1): Conv2d(48, 192, kernel_size=(1, 1), stride=(1, 1)) + (expand1x1_activation): ReLU(inplace=True) + (expand3x3): Conv2d(48, 192, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (expand3x3_activation): ReLU(inplace=True) + ) + (10): Fire( + (squeeze): Conv2d(384, 48, kernel_size=(1, 1), stride=(1, 1)) + (squeeze_activation): ReLU(inplace=True) + (expand1x1): Conv2d(48, 192, kernel_size=(1, 1), stride=(1, 1)) + (expand1x1_activation): ReLU(inplace=True) + (expand3x3): Conv2d(48, 192, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (expand3x3_activation): ReLU(inplace=True) + ) + (11): Fire( + (squeeze): Conv2d(384, 64, kernel_size=(1, 1), stride=(1, 1)) + (squeeze_activation): ReLU(inplace=True) + (expand1x1): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1)) + (expand1x1_activation): ReLU(inplace=True) + (expand3x3): Conv2d(64, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (expand3x3_activation): ReLU(inplace=True) + ) + ) + (final_convs): Sequential( + (0): MaxPool2d(kernel_size=2, stride=1, padding=0, dilation=1, ceil_mode=True) + (1): Conv2d(512, 64, kernel_size=(6, 6), stride=(1, 1), padding=(3, 3)) + (2): ReLU(inplace=True) + (3): Dropout(p=0.5, inplace=False) + (4): Conv2d(64, 3, kernel_size=(1, 1), stride=(1, 1)) + (5): ReLU(inplace=True) + ) +) \ No newline at end of file diff --git a/MiAlgo/trained_models/fc4_sum/fold_1/metrics.csv b/MiAlgo/trained_models/fc4_sum/fold_1/metrics.csv new file mode 100644 index 0000000000000000000000000000000000000000..ddad0d87e517676b8c22ca1b694e34b2b8767ab3 --- /dev/null +++ b/MiAlgo/trained_models/fc4_sum/fold_1/metrics.csv @@ -0,0 +1,2001 @@ +train_loss,val_loss,best_mean,best_median,best_trimean,best_bst25,best_wst25,best_wst5,mean,median,trimean,bst25,wst25,wst5 +13.322191773580304,9.083155216034795,9.083155216034795,7.293609142303467,7.512748599052429,4.709658007672492,16.66092489163081,18.698176383972168,9.083155216034795,7.293609142303467,7.512748599052429,4.709658007672492,16.66092489163081,18.698176383972168 +12.74128652338324,0,9.083155216034795,7.293609142303467,7.512748599052429,4.709658007672492,16.66092489163081,18.698176383972168,9.083155216034795,7.293609142303467,7.512748599052429,4.709658007672492,16.66092489163081,18.698176383972168 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b/MiAlgo/trained_models/fc4_sum/fold_1/network.txt new file mode 100644 index 0000000000000000000000000000000000000000..5d712dd9a675ffe55127e74cd4f0a678c7862c1e --- /dev/null +++ b/MiAlgo/trained_models/fc4_sum/fold_1/network.txt @@ -0,0 +1,73 @@ +FC4( + (backbone): Sequential( + (0): Conv2d(3, 64, kernel_size=(3, 3), stride=(2, 2)) + (1): ReLU(inplace=True) + (2): MaxPool2d(kernel_size=3, stride=2, padding=0, dilation=1, ceil_mode=True) + (3): Fire( + (squeeze): Conv2d(64, 16, kernel_size=(1, 1), stride=(1, 1)) + (squeeze_activation): ReLU(inplace=True) + (expand1x1): Conv2d(16, 64, kernel_size=(1, 1), stride=(1, 1)) + (expand1x1_activation): ReLU(inplace=True) + (expand3x3): Conv2d(16, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (expand3x3_activation): ReLU(inplace=True) + ) + (4): Fire( + (squeeze): Conv2d(128, 16, kernel_size=(1, 1), stride=(1, 1)) + (squeeze_activation): ReLU(inplace=True) + (expand1x1): Conv2d(16, 64, kernel_size=(1, 1), stride=(1, 1)) + (expand1x1_activation): ReLU(inplace=True) + (expand3x3): Conv2d(16, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (expand3x3_activation): ReLU(inplace=True) + ) + (5): MaxPool2d(kernel_size=3, stride=2, padding=0, dilation=1, ceil_mode=True) + (6): Fire( + (squeeze): Conv2d(128, 32, kernel_size=(1, 1), stride=(1, 1)) + (squeeze_activation): ReLU(inplace=True) + (expand1x1): Conv2d(32, 128, kernel_size=(1, 1), stride=(1, 1)) + (expand1x1_activation): ReLU(inplace=True) + (expand3x3): Conv2d(32, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (expand3x3_activation): ReLU(inplace=True) + ) + (7): Fire( + (squeeze): Conv2d(256, 32, kernel_size=(1, 1), stride=(1, 1)) + (squeeze_activation): ReLU(inplace=True) + (expand1x1): Conv2d(32, 128, kernel_size=(1, 1), stride=(1, 1)) + (expand1x1_activation): ReLU(inplace=True) + (expand3x3): Conv2d(32, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (expand3x3_activation): ReLU(inplace=True) + ) + (8): MaxPool2d(kernel_size=3, stride=2, padding=0, dilation=1, ceil_mode=True) + (9): Fire( + (squeeze): Conv2d(256, 48, kernel_size=(1, 1), stride=(1, 1)) + (squeeze_activation): ReLU(inplace=True) + (expand1x1): Conv2d(48, 192, kernel_size=(1, 1), stride=(1, 1)) + (expand1x1_activation): ReLU(inplace=True) + (expand3x3): Conv2d(48, 192, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (expand3x3_activation): ReLU(inplace=True) + ) + (10): Fire( + (squeeze): Conv2d(384, 48, kernel_size=(1, 1), stride=(1, 1)) + (squeeze_activation): ReLU(inplace=True) + (expand1x1): Conv2d(48, 192, kernel_size=(1, 1), stride=(1, 1)) + (expand1x1_activation): ReLU(inplace=True) + (expand3x3): Conv2d(48, 192, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (expand3x3_activation): ReLU(inplace=True) + ) + (11): Fire( + (squeeze): Conv2d(384, 64, kernel_size=(1, 1), stride=(1, 1)) + (squeeze_activation): ReLU(inplace=True) + (expand1x1): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1)) + (expand1x1_activation): ReLU(inplace=True) + (expand3x3): Conv2d(64, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (expand3x3_activation): ReLU(inplace=True) + ) + ) + (final_convs): Sequential( + (0): MaxPool2d(kernel_size=2, stride=1, padding=0, dilation=1, ceil_mode=True) + (1): Conv2d(512, 64, kernel_size=(6, 6), stride=(1, 1), padding=(3, 3)) + (2): ReLU(inplace=True) + (3): Dropout(p=0.5, inplace=False) + (4): Conv2d(64, 3, kernel_size=(1, 1), stride=(1, 1)) + (5): ReLU(inplace=True) + ) +) \ No newline at end of file diff --git a/MiAlgo/trained_models/fc4_sum/fold_2/metrics.csv b/MiAlgo/trained_models/fc4_sum/fold_2/metrics.csv new file mode 100644 index 0000000000000000000000000000000000000000..81bd1874a6b048ba5a1384a82e23fecabd6e8f34 --- /dev/null +++ b/MiAlgo/trained_models/fc4_sum/fold_2/metrics.csv @@ -0,0 +1,2001 @@ +train_loss,val_loss,best_mean,best_median,best_trimean,best_bst25,best_wst25,best_wst5,mean,median,trimean,bst25,wst25,wst5 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b/MiAlgo/trained_models/fc4_sum/fold_2/model.pth @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:39f21cad637b5a2130bd144c21dfec20aa050ee179f38ff2f65b68afb707ed46 +size 6839231 diff --git a/MiAlgo/trained_models/fc4_sum/fold_2/network.txt b/MiAlgo/trained_models/fc4_sum/fold_2/network.txt new file mode 100644 index 0000000000000000000000000000000000000000..5d712dd9a675ffe55127e74cd4f0a678c7862c1e --- /dev/null +++ b/MiAlgo/trained_models/fc4_sum/fold_2/network.txt @@ -0,0 +1,73 @@ +FC4( + (backbone): Sequential( + (0): Conv2d(3, 64, kernel_size=(3, 3), stride=(2, 2)) + (1): ReLU(inplace=True) + (2): MaxPool2d(kernel_size=3, stride=2, padding=0, dilation=1, ceil_mode=True) + (3): Fire( + (squeeze): Conv2d(64, 16, kernel_size=(1, 1), stride=(1, 1)) + (squeeze_activation): ReLU(inplace=True) + (expand1x1): Conv2d(16, 64, kernel_size=(1, 1), stride=(1, 1)) + (expand1x1_activation): ReLU(inplace=True) + (expand3x3): Conv2d(16, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (expand3x3_activation): ReLU(inplace=True) + ) + (4): Fire( + (squeeze): Conv2d(128, 16, kernel_size=(1, 1), stride=(1, 1)) + (squeeze_activation): ReLU(inplace=True) + (expand1x1): Conv2d(16, 64, kernel_size=(1, 1), stride=(1, 1)) + (expand1x1_activation): ReLU(inplace=True) + (expand3x3): Conv2d(16, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (expand3x3_activation): ReLU(inplace=True) + ) + (5): MaxPool2d(kernel_size=3, stride=2, padding=0, dilation=1, ceil_mode=True) + (6): Fire( + (squeeze): Conv2d(128, 32, kernel_size=(1, 1), stride=(1, 1)) + (squeeze_activation): ReLU(inplace=True) + (expand1x1): Conv2d(32, 128, kernel_size=(1, 1), stride=(1, 1)) + (expand1x1_activation): ReLU(inplace=True) + (expand3x3): Conv2d(32, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (expand3x3_activation): ReLU(inplace=True) + ) + (7): Fire( + (squeeze): Conv2d(256, 32, kernel_size=(1, 1), stride=(1, 1)) + (squeeze_activation): ReLU(inplace=True) + (expand1x1): Conv2d(32, 128, kernel_size=(1, 1), stride=(1, 1)) + (expand1x1_activation): ReLU(inplace=True) + (expand3x3): Conv2d(32, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (expand3x3_activation): ReLU(inplace=True) + ) + (8): MaxPool2d(kernel_size=3, stride=2, padding=0, dilation=1, ceil_mode=True) + (9): Fire( + (squeeze): Conv2d(256, 48, kernel_size=(1, 1), stride=(1, 1)) + (squeeze_activation): ReLU(inplace=True) + (expand1x1): Conv2d(48, 192, kernel_size=(1, 1), stride=(1, 1)) + (expand1x1_activation): ReLU(inplace=True) + (expand3x3): Conv2d(48, 192, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (expand3x3_activation): ReLU(inplace=True) + ) + (10): Fire( + (squeeze): Conv2d(384, 48, kernel_size=(1, 1), stride=(1, 1)) + (squeeze_activation): ReLU(inplace=True) + (expand1x1): Conv2d(48, 192, kernel_size=(1, 1), stride=(1, 1)) + (expand1x1_activation): ReLU(inplace=True) + (expand3x3): Conv2d(48, 192, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (expand3x3_activation): ReLU(inplace=True) + ) + (11): Fire( + (squeeze): Conv2d(384, 64, kernel_size=(1, 1), stride=(1, 1)) + (squeeze_activation): ReLU(inplace=True) + (expand1x1): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1)) + (expand1x1_activation): ReLU(inplace=True) + (expand3x3): Conv2d(64, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) + (expand3x3_activation): ReLU(inplace=True) + ) + ) + (final_convs): Sequential( + (0): MaxPool2d(kernel_size=2, stride=1, padding=0, dilation=1, ceil_mode=True) + (1): Conv2d(512, 64, kernel_size=(6, 6), stride=(1, 1), padding=(3, 3)) + (2): ReLU(inplace=True) + (3): Dropout(p=0.5, inplace=False) + (4): Conv2d(64, 3, kernel_size=(1, 1), stride=(1, 1)) + (5): ReLU(inplace=True) + ) +) \ No newline at end of file diff --git a/MiAlgo/unet.py b/MiAlgo/unet.py new file mode 100644 index 0000000000000000000000000000000000000000..b3d46acf4d5486a6402bf30ae318b591e740ec18 --- /dev/null +++ b/MiAlgo/unet.py @@ -0,0 +1,147 @@ +#/usr/bin/env python +# -*- coding: utf-8 -*- +# @Time : 22-02-09 +# @Author : zhangyuqian@xiaomi.com + +import tensorflow.compat.v1 as tf +import tf_slim as slim + + +def relu(x): + return tf.nn.relu(x) + + +def upsample_and_sum(x1, x2,output_channels,in_channels): + pool_size = 2 + deconv_filter = tf.Variable(tf.truncated_normal([pool_size, pool_size, output_channels, in_channels], stddev=0.02)) + deconv = tf.nn.conv2d_transpose(x1, deconv_filter, tf.shape(x2), strides=[1, pool_size, pool_size, 1]) + + deconv_output = tf.add(deconv,x2) + return deconv_output + +def upsample_and_concat(x1, x2, output_channels, in_channels): + pool_size = 2 + deconv_filter = tf.Variable(tf.truncated_normal([pool_size, pool_size, output_channels, in_channels], stddev=0.02)) + deconv = tf.nn.conv2d_transpose(x1, deconv_filter, tf.shape(x2), strides=[1, pool_size, pool_size, 1]) + + deconv_output = tf.concat([deconv, x2], 3) + deconv_output.set_shape([None, None, None, output_channels * 2]) + + return deconv_output + + + + + +def sc_net_1f(input): + # scratch capture single frame denoise network + # unet_2down_res_relu_64c5 + + with slim.arg_scope([slim.conv2d], weights_initializer=slim.variance_scaling_initializer(), + weights_regularizer=slim.l1_regularizer(0.0001),biases_initializer = None): + + conv1 = slim.conv2d(input, 64, [3, 3], rate=1, activation_fn=relu, scope='conv1_1') + res_conv1 = slim.conv2d(conv1, 64, [3, 3], rate=1, activation_fn=relu, scope='res_conv1_1') + res_conv1 = slim.conv2d(res_conv1, 64, [3, 3], rate=1, activation_fn=relu, scope='res_conv1_2') + res_block1 = conv1 + res_conv1 + + + pool2 = slim.avg_pool2d(res_block1,[2,2],padding='SAME') + res_conv2 = slim.conv2d(pool2, 64, [3, 3], rate=1, activation_fn=relu, scope='res_conv2_1') + res_conv2 = slim.conv2d(res_conv2, 64, [3, 3], rate=1, activation_fn=relu, scope='res_conv2_2') + res_block2 = pool2 + res_conv2 + + pool3 = slim.avg_pool2d(res_block2,[2,2],padding='SAME') + res_conv3 = slim.conv2d(pool3, 64, [3, 3], rate=1, activation_fn=relu, scope='res_conv3_1') + res_conv3 = slim.conv2d(res_conv3, 64, [3, 3], rate=1, activation_fn=relu, scope='res_conv3_2') + res_block3 = pool3 + res_conv3 + + deconv1 = upsample_and_sum(res_block3, res_block2, 64, 64) + + conv4 = slim.conv2d(deconv1, 64, [3, 3], rate=1, stride=1, activation_fn=relu, scope='conv4_1') + res_conv4 = slim.conv2d(conv4, 64, [3, 3], rate=1, activation_fn=relu, scope='res_conv4_1') + res_conv4 = slim.conv2d(res_conv4, 64, [3, 3], rate=1, activation_fn=relu, scope='res_conv4_2') + res_block4 = conv4 + res_conv4 + + deconv2 = upsample_and_sum(res_block4, res_block1, 64, 64) + + conv5 = slim.conv2d(deconv2, 64, [3, 3], rate=1, stride=1, activation_fn=relu, scope='conv5_1') + res_conv5 = slim.conv2d(conv5, 64, [3, 3], rate=1, activation_fn=relu, scope='res_conv5_1') + res_conv5 = slim.conv2d(res_conv5, 64, [3, 3], rate=1, activation_fn=relu, scope='res_conv5_2') + res_block5 = conv5 + res_conv5 + + conv6 = slim.conv2d(res_block5, 64, [3, 3], rate=1, stride=1, activation_fn=relu, scope='conv6_1') + conv7 = slim.conv2d(conv6, 4, [3, 3], rate=1, stride=1, activation_fn=None, scope='conv7_1') + + out = conv7 + + return out + + + + +def sc_net(input): + + with slim.arg_scope([slim.conv2d], weights_initializer=slim.variance_scaling_initializer(), + weights_regularizer=slim.l1_regularizer(0.0001),biases_initializer = None): + + conv1 = slim.conv2d(input, 32, [3, 3], rate=1, activation_fn=relu, scope='g_conv1_1') + conv1 = slim.conv2d(conv1, 32, [3, 3], rate=1, activation_fn=relu, scope='g_conv1_2') + pool1 = slim.max_pool2d(conv1, [2, 2], padding='SAME') + + conv2 = slim.conv2d(pool1, 64, [3, 3], rate=1, activation_fn=relu, scope='g_conv2_1') + conv2 = slim.conv2d(conv2, 64, [3, 3], rate=1, activation_fn=relu, scope='g_conv2_2') + pool2 = slim.max_pool2d(conv2, [2, 2], padding='SAME') + + conv3 = slim.conv2d(pool2, 128, [3, 3], rate=1, activation_fn=relu, scope='g_conv3_1') + conv3 = slim.conv2d(conv3, 128, [3, 3], rate=1, activation_fn=relu, scope='g_conv3_2') + pool3 = slim.max_pool2d(conv3, [2, 2], padding='SAME') + + conv4 = slim.conv2d(pool3, 256, [3, 3], rate=1, activation_fn=relu, scope='g_conv4_1') + conv4 = slim.conv2d(conv4, 256, [3, 3], rate=1, activation_fn=relu, scope='g_conv4_2') + pool4 = slim.max_pool2d(conv4, [2, 2], padding='SAME') + + conv5 = slim.conv2d(pool4, 512, [3, 3], rate=1, activation_fn=relu, scope='g_conv5_1') + conv5 = slim.conv2d(conv5, 512, [3, 3], rate=1, activation_fn=relu, scope='g_conv5_2') + + up6 = upsample_and_concat(conv5, conv4, 256, 512) + conv6 = slim.conv2d(up6, 256, [3, 3], rate=1, activation_fn=relu, scope='g_conv6_1') + conv6 = slim.conv2d(conv6, 256, [3, 3], rate=1, activation_fn=relu, scope='g_conv6_2') + + up7 = upsample_and_concat(conv6, conv3, 128, 256) + conv7 = slim.conv2d(up7, 128, [3, 3], rate=1, activation_fn=relu, scope='g_conv7_1') + conv7 = slim.conv2d(conv7, 128, [3, 3], rate=1, activation_fn=relu, scope='g_conv7_2') + + up8 = upsample_and_concat(conv7, conv2, 64, 128) + conv8 = slim.conv2d(up8, 64, [3, 3], rate=1, activation_fn=relu, scope='g_conv8_1') + conv8 = slim.conv2d(conv8, 64, [3, 3], rate=1, activation_fn=relu, scope='g_conv8_2') + + up9 = upsample_and_concat(conv8, conv1, 32, 64) + conv9 = slim.conv2d(up9, 32, [3, 3], rate=1, activation_fn=relu, scope='g_conv9_1') + conv9 = slim.conv2d(conv9, 32, [3, 3], rate=1, activation_fn=relu, scope='g_conv9_2') + + conv10 = slim.conv2d(conv9, 4, [1, 1], rate=1, activation_fn=relu, scope='g_conv10') + + out = conv10 + + return out + + + + + + + + + + + + + + + + + + + + diff --git a/MiAlgo/utils.py b/MiAlgo/utils.py new file mode 100644 index 0000000000000000000000000000000000000000..bc1ab0cadc6023c01867b86f7a0fdf1c93490077 --- /dev/null +++ b/MiAlgo/utils.py @@ -0,0 +1,320 @@ +import numpy as np +import cv2 +import cv2 as cv + +import os +import json +from fractions import Fraction +from exifread.utils import Ratio +from scipy.io import loadmat +from PIL import Image, ImageOps +import torch +from copy import deepcopy + + +def json_read(fname, **kwargs): + with open(fname) as j: + data = json.load(j, **kwargs) + return data + +def fraction_from_json(json_object): + if 'Fraction' in json_object: + return Fraction(*json_object['Fraction']) + return json_object + +def fractions2floats(fractions): + floats = [] + for fraction in fractions: + floats.append(float(fraction.numerator) / fraction.denominator) + return floats + +def ratios2floats(ratios): + floats = [] + for ratio in ratios: + floats.append(float(ratio.num) / ratio.den) + return floats + +def normalize(raw_image, black_level, white_level, bl_fix=0): + if type(black_level) is list and len(black_level) == 1: + black_level = float(black_level[0]) + if type(white_level) is list and len(white_level) == 1: + white_level = float(white_level[0]) + black_level_mask = black_level + if type(black_level) is list and len(black_level) == 4: + if type(black_level[0]) is Ratio: + black_level = ratios2floats(black_level) + if type(black_level[0]) is Fraction: + black_level = fractions2floats(black_level) + black_level_mask = np.zeros(raw_image.shape) + idx2by2 = [[0, 0], [0, 1], [1, 0], [1, 1]] + step2 = 2 + for i, idx in enumerate(idx2by2): + black_level_mask[idx[0]::step2, idx[1]::step2] = black_level[i] + + if bl_fix: + black_level_mask = black_level_mask + bl_fix + + normalized_image = (raw_image.astype(np.float32) - black_level_mask) / (white_level - black_level_mask) + normalized_image = np.clip(normalized_image, 0, 1) + return normalized_image + +def bayer_to_offsets(bayer_pattern): + """ + Transform bayer pattern to offsets in order 'RGrBGb' + n.b. Support 'RGrBGb' bayer pattern only. + Args: + bayer_pattern: string, e.g. 'rggb'. Must be one of 'rggb', 'grbg', 'gbrg', 'bggr' + + Returns: + offsets: packed raw image with 4 channels + """ + bayer_pattern = bayer_pattern.lower() + assert bayer_pattern in ['rggb', 'grbg', 'gbrg', 'bggr'], 'WRONG BAYER PATTERN!' + + if bayer_pattern == 'rggb': + offsets = [[0,0],[0,1],[1,1],[1,0]] + elif bayer_pattern == 'grbg': + offsets = [[0,1],[0,0],[1,0],[1,1]] + elif bayer_pattern == 'gbrg': + offsets = [[1,0],[0,0],[0,1],[1,1]] + else: #bayer_pattern == 'bggr': + offsets = [[1,1],[0,1],[0,0],[1,0]] + + return offsets + +def pack_raw_to_4ch(rawim, offsets): + """ + Pack raw to h/2 x w/2 x 4n with order "RGrBGb..." RGBG RGBG RGBG + n.b. Support ordinary bayer pattern only. + Args: + rawim: numpy.ndarray in shape (h, w, ...) + bayer_pattern: string, e.g. "rggb". Must be one of "rggb", "grbg", "gbrg", "bggr" + + Returns: + out: packed raw image with 4n channels + """ + + + if rawim.ndim == 2: + rawim = np.expand_dims(rawim, axis=-1) + rawim_pack = np.concatenate((rawim[offsets[0][0]::2, offsets[0][1]::2], + rawim[offsets[1][0]::2, offsets[1][1]::2], + rawim[offsets[2][0]::2, offsets[2][1]::2], + rawim[offsets[3][0]::2, offsets[3][1]::2]), axis=-1) + elif rawim.ndim ==3: + frame_num = rawim.shape[2] + rawim_pack = np.zeros((int(rawim.shape[0]/2), int(rawim.shape[1]/2), rawim.shape[2] * 4)) + for i in range(frame_num): + rawim_temp = rawim[:,:,i] + rawim_temp = np.expand_dims(rawim_temp, axis=-1) + rawim_temp_pack = np.concatenate((rawim_temp[offsets[0][0]::2, offsets[0][1]::2], + rawim_temp[offsets[1][0]::2, offsets[1][1]::2], + rawim_temp[offsets[2][0]::2, offsets[2][1]::2], + rawim_temp[offsets[3][0]::2, offsets[3][1]::2]), axis=-1) + + rawim_pack[:, :, i * 4:(i + 1) * 4] = rawim_temp_pack + + return rawim_pack + + + +def apply_color_space_transform(demosaiced_image, color_matrix_1, color_matrix_2): + if isinstance(color_matrix_1[0], Fraction): + color_matrix_1 = fractions2floats(color_matrix_1) + if isinstance(color_matrix_2[0], Fraction): + color_matrix_2 = fractions2floats(color_matrix_2) + xyz2cam1 = np.reshape(np.asarray(color_matrix_1), (3, 3)) + # normalize rows (needed?) + xyz2cam1 = xyz2cam1 / np.sum(xyz2cam1, axis=1, keepdims=True) + # inverse + cam2xyz1 = np.linalg.inv(xyz2cam1) + # for now, use one matrix # TODO: interpolate btween both + # simplified matrix multiplication + + xyz_image = cam2xyz1[np.newaxis, np.newaxis, :, :] * \ + demosaiced_image[:, :, np.newaxis, :] + xyz_image = np.sum(xyz_image, axis=-1) + xyz_image = np.clip(xyz_image, 0.0, 1.0) + return xyz_image + + + + +def transform_xyz_to_srgb(xyz_image): + xyz2srgb = np.array([[3.0799, -1.5372, -0.5428], + [-0.9212, 1.8760, 0.0452], + [0.0529, -0.2040, 1.1512]]) + srgb_image = xyz2srgb[np.newaxis, np.newaxis, + :, :] * xyz_image[:, :, np.newaxis, :] + srgb_image = np.sum(srgb_image, axis=-1) + srgb_image = np.clip(srgb_image, 0.0, 1.0) + return srgb_image + + +def apply_gamma(x): + # return x ** (1.0 / 2.4) + x = x.copy() + gray = cv2.cvtColor(x.astype(np.float32), cv2.COLOR_BGR2GRAY) + idx = gray <= 0.0031308 + idx = np.stack([idx, idx, idx], axis=-1) + x[idx] *= 12.92 + x[idx == False] = (x[idx == False] ** (1.0 / 2.4)) * 1.055 - 0.055 + return x + + +def fix_orientation(image, orientation): + type1 = "Horizontal (normal)" + type2 = "Mirror horizontal" + type3 = "Rotate 180" + type4 = "Mirror vertical" + type5 = "Mirror horizontal and rotate 270 CW" + type6 = "Rotate 90 CW" + type7 = "Mirror horizontal and rotate 90 CW" + type8 = "Rotate 270 CW" + + # if type(orientation) is list: + # orientation = orientation[0] + + if orientation == type1: + pass + elif orientation == type2: + image = cv.flip(image, 0) + elif orientation == type3: + image = cv.rotate(image, cv.ROTATE_180) + elif orientation == type4: + image = cv.flip(image, 1) + elif orientation == type5: + image = cv.flip(image, 0) + image = cv.rotate(image, cv.ROTATE_90_COUNTERCLOCKWISE) + elif orientation == type6: + image = cv.rotate(image, cv.ROTATE_90_CLOCKWISE) + elif orientation == type7: + image = cv.flip(image, 0) + image = cv.rotate(image, cv.ROTATE_90_CLOCKWISE) + elif orientation == type8: + image = cv.rotate(image, cv.ROTATE_90_COUNTERCLOCKWISE) + else: + raise + + return image + + +def apply_tone_map(x, tone_mapping='Base'): + if tone_mapping == 'Flash': + return perform_flash(x, perform_gamma_correction=0)/255. + elif tone_mapping == 'Storm': + return perform_storm(x, perform_gamma_correction=0)/255. + elif tone_mapping == 'Drago': + tonemap = cv2.createTonemapDrago() + return tonemap.process(x.astype(np.float32)) + elif tone_mapping == 'Mantiuk': + tonemap = cv2.createTonemapMantiuk() + return tonemap.process(x.astype(np.float32)) + elif tone_mapping == 'Reinhard': + tonemap = cv2.createTonemapReinhard() + return tonemap.process(x.astype(np.float32)) + elif tone_mapping == 'Linear': + return np.clip(x/np.sort(x.flatten())[-50000], 0, 1) + elif tone_mapping == 'Base': + tone_curve = loadmat(os.path.join(os.path.dirname( + os.path.realpath(__file__)), 'tone_curve.mat')) + tone_curve = tone_curve['tc'] + x = np.round(x * (len(tone_curve) - 1)).astype(int) + tone_mapped_image = np.squeeze(tone_curve[x]) + return tone_mapped_image + else: + raise ValueError( + 'Bad tone_mapping option value! Use the following options: "Base", "Flash", "Storm", "Linear", "Drago", "Mantiuk", "Reinhard"') + +def autocontrast_using_pil(img, cutoff=2): + img_uint8 = np.clip(255*img, 0, 255).astype(np.uint8) + img_pil = Image.fromarray(img_uint8) + img_pil = ImageOps.autocontrast(img_pil, cutoff=cutoff) + output_image = np.array(img_pil).astype(np.float32) / 255 + return output_image + +def usm_sharp(img, weight=0.666, radius=10, threshold=10): + if radius % 2 == 0: + radius += 1 + blur = cv2.GaussianBlur(img, (radius, radius), 0) + residual = img - blur + mask = np.abs(residual) * 255 > threshold + mask = mask.astype('float32') + soft_mask = cv2.GaussianBlur(mask, (radius, radius), 0) + + sharp = img + weight * residual + sharp = np.clip(sharp, 0, 1) + return soft_mask * sharp + (1 - soft_mask) * img + + +def white_balance(demosaic_img, as_shot_neutral): + if type(as_shot_neutral[0]) is Ratio: + as_shot_neutral = ratios2floats(as_shot_neutral) + + as_shot_neutral = np.asarray(as_shot_neutral) + # transform vector into matrix + if as_shot_neutral.shape == (3,): + as_shot_neutral = np.diag(1./as_shot_neutral) + + assert as_shot_neutral.shape == (3, 3) + white_balanced_image = np.dot(demosaic_img, as_shot_neutral.T) + white_balanced_image = np.clip(white_balanced_image, 0.0, 1.0) + + return white_balanced_image + + +def get_net(NET, checkpoint_path, device): + net = NET() + load_net = torch.load(checkpoint_path, map_location="cpu") + + try: + load_net = load_net['params'] + except: + pass + + net.load_state_dict(load_net, strict=True) + net = net.to(device) + net = net.eval() + return net + + + +def pyrblend(img1, img2, mask): + img1 = img1.astype(np.float32) + img2 = img2.astype(np.float32) + mask = mask.astype(np.float32) + + # 图像向下取样, 构造高斯金字塔: [原图，下取样1次，下取样2次，下取样3次，下取样4次] + levels = 5 # 高斯金字塔层数 + gaussPyr1, gaussPyr2 = [img1], [img2] # 原始图像为高斯金字塔第 0 层 + for i in range(1, levels): # 高斯金字塔共 5 层: 0,1,2,3,4 + gaussPyr1.append(cv2.pyrDown(gaussPyr1[i-1])) # 计算第 i 层高斯金字塔 + gaussPyr2.append(cv2.pyrDown(gaussPyr2[i-1])) + + # 图像向上取样, 构造拉普拉斯金字塔 [第1层残差，第2层残差，第3层残差，第4层残差] + lapPyr1, lapPyr2 = [], [] # 从最顶层开始恢复 + for i in range(levels-1): # 拉普拉斯金字塔有 4 层: 0,1,2,3 + lapPyr1.append(gaussPyr1[i] - cv2.pyrUp(gaussPyr1[i+1])) + lapPyr2.append(gaussPyr2[i] - cv2.pyrUp(gaussPyr2[i+1])) + + # 拉普拉斯金字塔左右拼接 + lapStack = [] + for i in range(levels-1): # 拉普拉斯金字塔共 4 层: 0,1,2,3 + rows, cols, channel = lapPyr1[i].shape + lmask = cv2.resize(mask, (cols, rows), interpolation=cv2.INTER_AREA) + lmask = lmask[:, :, np.newaxis] + + splicing = lapPyr1[i] * (1-lmask) + lapPyr2[i]*lmask + lapStack.append(splicing) + + # 由拼接后的Laplace金字塔恢复原图像 + rows, cols, channel = gaussPyr1[-1].shape # 高斯金字塔顶层 G4:(32,32) + lmask = cv2.resize(mask, (cols, rows), interpolation=cv2.INTER_AREA) + lmask = lmask[:, :, np.newaxis] + stackG4 = gaussPyr1[-1] * (1-lmask) + gaussPyr2[-1]*lmask # 拼接高斯金字塔顶层 + stackG3 = lapStack[3] + cv2.pyrUp(stackG4) # stackG3:(64,64) + stackG2 = lapStack[2] + cv2.pyrUp(stackG3) # stackG2:(128,128) + stackG1 = lapStack[1] + cv2.pyrUp(stackG2) # stackG1:(256,256) + stackG0 = lapStack[0] + cv2.pyrUp(stackG1) # stackG0:(512,512) + + return stackG0 \ No newline at end of file