File size: 2,892 Bytes
434b0b0 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 | import logging
import os
import torch
from torchvision import transforms
import numpy as np
import random
import cv2
from PIL import Image
def path_to_image(path, size=(1024, 1024), color_type=["rgb", "gray"][0]):
if color_type.lower() == "rgb":
image = cv2.imread(path)
elif color_type.lower() == "gray":
image = cv2.imread(path, cv2.IMREAD_GRAYSCALE)
else:
print("Select the color_type to return, either to RGB or gray image.")
return
if size:
image = cv2.resize(image, size, interpolation=cv2.INTER_LINEAR)
if color_type.lower() == "rgb":
image = Image.fromarray(cv2.cvtColor(image, cv2.COLOR_BGR2RGB)).convert("RGB")
else:
image = Image.fromarray(image).convert("L")
return image
def check_state_dict(state_dict, unwanted_prefixes=["_orig_mod.", "module."]):
for k, v in list(state_dict.items()):
for unwanted_prefix in unwanted_prefixes:
if k.startswith(unwanted_prefix):
state_dict[k[len(unwanted_prefix) :]] = state_dict.pop(k)
break
return state_dict
def generate_smoothed_gt(gts):
epsilon = 0.001
new_gts = (1 - epsilon) * gts + epsilon / 2
return new_gts
class Logger:
def __init__(self, path="log.txt"):
self.logger = logging.getLogger("BiRefNet")
self.file_handler = logging.FileHandler(path, "w")
self.stdout_handler = logging.StreamHandler()
self.stdout_handler.setFormatter(
logging.Formatter("%(asctime)s %(levelname)s %(message)s")
)
self.file_handler.setFormatter(
logging.Formatter("%(asctime)s %(levelname)s %(message)s")
)
self.logger.addHandler(self.file_handler)
self.logger.addHandler(self.stdout_handler)
self.logger.setLevel(logging.INFO)
self.logger.propagate = False
def info(self, txt):
self.logger.info(txt)
def close(self):
self.file_handler.close()
self.stdout_handler.close()
class AverageMeter(object):
"""Computes and stores the average and current value"""
def __init__(self):
self.reset()
def reset(self):
self.val = 0.0
self.avg = 0.0
self.sum = 0.0
self.count = 0.0
def update(self, val, n=1):
self.val = val
self.sum += val * n
self.count += n
self.avg = self.sum / self.count
def save_checkpoint(state, path, filename="latest.pth"):
torch.save(state, os.path.join(path, filename))
def save_tensor_img(tenor_im, path):
im = tenor_im.cpu().clone()
im = im.squeeze(0)
tensor2pil = transforms.ToPILImage()
im = tensor2pil(im)
im.save(path)
def set_seed(seed):
torch.manual_seed(seed)
torch.cuda.manual_seed_all(seed)
np.random.seed(seed)
random.seed(seed)
torch.backends.cudnn.deterministic = True
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