forked from mrq/DL-Art-School
511 lines
17 KiB
Python
511 lines
17 KiB
Python
import os
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import sys
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import time
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import math
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import torch.nn.functional as F
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from datetime import datetime
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import random
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import logging
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from collections import OrderedDict
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import numpy as np
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import cv2
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import torch
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from torchvision.utils import make_grid
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from shutil import get_terminal_size
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import scp
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import paramiko
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from torch.utils.checkpoint import checkpoint
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from torch._six import inf
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import yaml
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from trainer import networks
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try:
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from yaml import CLoader as Loader, CDumper as Dumper
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except ImportError:
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from yaml import Loader, Dumper
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loaded_options = None
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def OrderedYaml():
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'''yaml orderedDict support'''
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_mapping_tag = yaml.resolver.BaseResolver.DEFAULT_MAPPING_TAG
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def dict_representer(dumper, data):
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return dumper.represent_dict(data.items())
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def dict_constructor(loader, node):
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return OrderedDict(loader.construct_pairs(node))
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Dumper.add_representer(OrderedDict, dict_representer)
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Loader.add_constructor(_mapping_tag, dict_constructor)
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return Loader, Dumper
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####################
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# miscellaneous
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####################
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# Conditionally uses torch's checkpoint functionality if it is enabled in the opt file.
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def checkpoint(fn, *args):
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if loaded_options is None:
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enabled = False
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else:
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enabled = loaded_options['checkpointing_enabled'] if 'checkpointing_enabled' in loaded_options.keys() else True
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if enabled:
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return torch.utils.checkpoint.checkpoint(fn, *args)
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else:
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return fn(*args)
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def sequential_checkpoint(fn, partitions, *args):
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if loaded_options is None:
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enabled = False
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else:
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enabled = loaded_options['checkpointing_enabled'] if 'checkpointing_enabled' in loaded_options.keys() else True
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if enabled:
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return torch.utils.checkpoint.checkpoint_sequential(fn, partitions, *args)
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else:
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return fn(*args)
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# A fancy alternative to if <flag> checkpoint() else <call>
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def possible_checkpoint(opt_en, fn, *args):
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if loaded_options is None:
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enabled = False
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else:
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enabled = loaded_options['checkpointing_enabled'] if 'checkpointing_enabled' in loaded_options.keys() else True
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if enabled and opt_en:
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return torch.utils.checkpoint.checkpoint(fn, *args)
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else:
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return fn(*args)
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def get_timestamp():
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return datetime.now().strftime('%y%m%d-%H%M%S')
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def mkdir(path):
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if not os.path.exists(path):
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os.makedirs(path)
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def mkdirs(paths):
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if isinstance(paths, str):
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mkdir(paths)
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else:
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for path in paths:
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mkdir(path)
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def mkdir_and_rename(path):
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if os.path.exists(path):
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new_name = path + '_archived_' + get_timestamp()
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print('Path already exists. Rename it to [{:s}]'.format(new_name))
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logger = logging.getLogger('base')
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logger.info('Path already exists. Rename it to [{:s}]'.format(new_name))
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os.rename(path, new_name)
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os.makedirs(path)
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def set_random_seed(seed):
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random.seed(seed)
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np.random.seed(seed)
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torch.manual_seed(seed)
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torch.cuda.manual_seed_all(seed)
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def setup_logger(logger_name, root, phase, level=logging.INFO, screen=False, tofile=False):
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'''set up logger'''
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lg = logging.getLogger(logger_name)
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formatter = logging.Formatter('%(asctime)s.%(msecs)03d - %(levelname)s: %(message)s',
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datefmt='%y-%m-%d %H:%M:%S')
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lg.setLevel(level)
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if tofile:
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log_file = os.path.join(root, phase + '_{}.log'.format(get_timestamp()))
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fh = logging.FileHandler(log_file, mode='w')
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fh.setFormatter(formatter)
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lg.addHandler(fh)
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if screen:
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sh = logging.StreamHandler()
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sh.setFormatter(formatter)
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lg.addHandler(sh)
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def copy_files_to_server(host, user, password, files, remote_path):
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client = paramiko.SSHClient()
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client.load_system_host_keys()
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client.set_missing_host_key_policy(paramiko.AutoAddPolicy())
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client.connect(host, username=user, password=password)
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scpclient = scp.SCPClient(client.get_transport())
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scpclient.put(files, remote_path)
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def get_files_from_server(host, user, password, remote_path, local_path):
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client = paramiko.SSHClient()
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client.load_system_host_keys()
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client.set_missing_host_key_policy(paramiko.AutoAddPolicy())
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client.connect(host, username=user, password=password)
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scpclient = scp.SCPClient(client.get_transport())
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scpclient.get(remote_path, local_path)
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####################
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# image convert
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####################
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def crop_border(img_list, crop_border):
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"""Crop borders of images
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Args:
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img_list (list [Numpy]): HWC
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crop_border (int): crop border for each end of height and weight
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Returns:
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(list [Numpy]): cropped image list
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"""
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if crop_border == 0:
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return img_list
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else:
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return [v[crop_border:-crop_border, crop_border:-crop_border] for v in img_list]
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def tensor2img(tensor, out_type=np.uint8, min_max=(0, 1)):
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'''
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Converts a torch Tensor into an image Numpy array
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Input: 4D(B,(3/1),H,W), 3D(C,H,W), or 2D(H,W), any range, RGB channel order
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Output: 3D(H,W,C) or 2D(H,W), [0,255], np.uint8 (default)
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'''
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tensor = tensor.squeeze().float().cpu().clamp_(*min_max) # clamp
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tensor = (tensor - min_max[0]) / (min_max[1] - min_max[0]) # to range [0,1]
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n_dim = tensor.dim()
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if n_dim == 4:
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n_img = len(tensor)
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img_np = make_grid(tensor, nrow=int(math.sqrt(n_img)), normalize=False).numpy()
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img_np = np.transpose(img_np[[2, 1, 0], :, :], (1, 2, 0)) # HWC, BGR
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elif n_dim == 3:
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img_np = tensor.numpy()
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img_np = np.transpose(img_np[[2, 1, 0], :, :], (1, 2, 0)) # HWC, BGR
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elif n_dim == 2:
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img_np = tensor.numpy()
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else:
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raise TypeError(
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'Only support 4D, 3D and 2D tensor. But received with dimension: {:d}'.format(n_dim))
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if out_type == np.uint8:
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img_np = (img_np * 255.0).round()
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# Important. Unlike matlab, numpy.unit8() WILL NOT round by default.
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return img_np.astype(out_type)
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def save_img(img, img_path, mode='RGB'):
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cv2.imwrite(img_path, img)
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def DUF_downsample(x, scale=4):
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"""Downsamping with Gaussian kernel used in the DUF official code
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Args:
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x (Tensor, [B, T, C, H, W]): frames to be downsampled.
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scale (int): downsampling factor: 2 | 3 | 4.
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"""
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assert scale in [2, 3, 4], 'Scale [{}] is not supported'.format(scale)
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def gkern(kernlen=13, nsig=1.6):
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import scipy.ndimage.filters as fi
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inp = np.zeros((kernlen, kernlen))
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# set element at the middle to one, a dirac delta
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inp[kernlen // 2, kernlen // 2] = 1
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# gaussian-smooth the dirac, resulting in a gaussian filter mask
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return fi.gaussian_filter(inp, nsig)
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B, T, C, H, W = x.size()
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x = x.view(-1, 1, H, W)
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pad_w, pad_h = 6 + scale * 2, 6 + scale * 2 # 6 is the pad of the gaussian filter
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r_h, r_w = 0, 0
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if scale == 3:
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r_h = 3 - (H % 3)
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r_w = 3 - (W % 3)
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x = F.pad(x, [pad_w, pad_w + r_w, pad_h, pad_h + r_h], 'reflect')
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gaussian_filter = torch.from_numpy(gkern(13, 0.4 * scale)).type_as(x).unsqueeze(0).unsqueeze(0)
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x = F.conv2d(x, gaussian_filter, stride=scale)
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x = x[:, :, 2:-2, 2:-2]
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x = x.view(B, T, C, x.size(2), x.size(3))
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return x
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def single_forward(model, inp):
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"""PyTorch model forward (single test), it is just a simple warpper
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Args:
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model (PyTorch model)
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inp (Tensor): inputs defined by the model
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Returns:
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output (Tensor): outputs of the model. float, in CPU
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"""
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with torch.no_grad():
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model_output = model(inp)
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if isinstance(model_output, list) or isinstance(model_output, tuple):
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output = model_output[0]
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else:
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output = model_output
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output = output.data.float().cpu()
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return output
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def flipx4_forward(model, inp):
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"""Flip testing with X4 self ensemble, i.e., normal, flip H, flip W, flip H and W
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Args:
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model (PyTorch model)
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inp (Tensor): inputs defined by the model
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Returns:
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output (Tensor): outputs of the model. float, in CPU
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"""
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# normal
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output_f = single_forward(model, inp)
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# flip W
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output = single_forward(model, torch.flip(inp, (-1, )))
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output_f = output_f + torch.flip(output, (-1, ))
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# flip H
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output = single_forward(model, torch.flip(inp, (-2, )))
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output_f = output_f + torch.flip(output, (-2, ))
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# flip both H and W
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output = single_forward(model, torch.flip(inp, (-2, -1)))
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output_f = output_f + torch.flip(output, (-2, -1))
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return output_f / 4
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####################
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# metric
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####################
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def calculate_psnr(img1, img2):
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# img1 and img2 have range [0, 255]
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img1 = img1.astype(np.float64)
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img2 = img2.astype(np.float64)
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mse = np.mean((img1 - img2)**2)
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if mse == 0:
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return float('inf')
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return 20 * math.log10(255.0 / math.sqrt(mse))
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def ssim(img1, img2):
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C1 = (0.01 * 255)**2
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C2 = (0.03 * 255)**2
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img1 = img1.astype(np.float64)
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img2 = img2.astype(np.float64)
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kernel = cv2.getGaussianKernel(11, 1.5)
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window = np.outer(kernel, kernel.transpose())
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mu1 = cv2.filter2D(img1, -1, window)[5:-5, 5:-5] # valid
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mu2 = cv2.filter2D(img2, -1, window)[5:-5, 5:-5]
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mu1_sq = mu1**2
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mu2_sq = mu2**2
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mu1_mu2 = mu1 * mu2
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sigma1_sq = cv2.filter2D(img1**2, -1, window)[5:-5, 5:-5] - mu1_sq
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sigma2_sq = cv2.filter2D(img2**2, -1, window)[5:-5, 5:-5] - mu2_sq
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sigma12 = cv2.filter2D(img1 * img2, -1, window)[5:-5, 5:-5] - mu1_mu2
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ssim_map = ((2 * mu1_mu2 + C1) * (2 * sigma12 + C2)) / ((mu1_sq + mu2_sq + C1) *
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(sigma1_sq + sigma2_sq + C2))
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return ssim_map.mean()
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def calculate_ssim(img1, img2):
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'''calculate SSIM
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the same outputs as MATLAB's
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img1, img2: [0, 255]
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'''
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if not img1.shape == img2.shape:
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raise ValueError('Input images must have the same dimensions.')
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if img1.ndim == 2:
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return ssim(img1, img2)
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elif img1.ndim == 3:
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if img1.shape[2] == 3:
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ssims = []
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for i in range(3):
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ssims.append(ssim(img1, img2))
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return np.array(ssims).mean()
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elif img1.shape[2] == 1:
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return ssim(np.squeeze(img1), np.squeeze(img2))
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else:
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raise ValueError('Wrong input image dimensions.')
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class ProgressBar(object):
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'''A progress bar which can print the progress
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modified from https://github.com/hellock/cvbase/blob/master/cvbase/progress.py
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'''
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def __init__(self, task_num=0, bar_width=50, start=True):
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self.task_num = task_num
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max_bar_width = self._get_max_bar_width()
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self.bar_width = (bar_width if bar_width <= max_bar_width else max_bar_width)
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self.completed = 0
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if start:
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self.start()
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def _get_max_bar_width(self):
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terminal_width, _ = get_terminal_size()
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max_bar_width = min(int(terminal_width * 0.6), terminal_width - 50)
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if max_bar_width < 10:
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print('terminal width is too small ({}), please consider widen the terminal for better '
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'progressbar visualization'.format(terminal_width))
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max_bar_width = 10
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return max_bar_width
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def start(self):
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if self.task_num > 0:
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sys.stdout.write('[{}] 0/{}, elapsed: 0s, ETA:\n{}\n'.format(
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' ' * self.bar_width, self.task_num, 'Start...'))
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else:
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sys.stdout.write('completed: 0, elapsed: 0s')
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sys.stdout.flush()
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self.start_time = time.time()
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def update(self, msg='In progress...'):
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self.completed += 1
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elapsed = time.time() - self.start_time
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fps = self.completed / elapsed
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if self.task_num > 0:
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percentage = self.completed / float(self.task_num)
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eta = int(elapsed * (1 - percentage) / percentage + 0.5)
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mark_width = int(self.bar_width * percentage)
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bar_chars = '>' * mark_width + '-' * (self.bar_width - mark_width)
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sys.stdout.write('\033[2F') # cursor up 2 lines
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sys.stdout.write('\033[J') # clean the output (remove extra chars since last display)
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sys.stdout.write('[{}] {}/{}, {:.1f} task/s, elapsed: {}s, ETA: {:5}s\n{}\n'.format(
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bar_chars, self.completed, self.task_num, fps, int(elapsed + 0.5), eta, msg))
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else:
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sys.stdout.write('completed: {}, elapsed: {}s, {:.1f} tasks/s'.format(
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self.completed, int(elapsed + 0.5), fps))
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sys.stdout.flush()
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# Recursively detaches all tensors in a tree of lists, dicts and tuples and returns the same structure.
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def recursively_detach(v):
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if isinstance(v, torch.Tensor):
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return v.detach().clone()
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elif isinstance(v, list) or isinstance(v, tuple):
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out = [recursively_detach(i) for i in v]
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if isinstance(v, tuple):
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return tuple(out)
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return out
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elif isinstance(v, dict):
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out = {}
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for k, t in v.items():
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out[k] = recursively_detach(t)
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return out
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def opt_get(opt, keys, default=None):
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assert not isinstance(keys, str) # Common mistake, better to assert.
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if opt is None:
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return default
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ret = opt
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for k in keys:
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ret = ret.get(k, None)
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if ret is None:
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return default
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return ret
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def denormalize(x, mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]):
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ten = x.clone().permute(1, 2, 3, 0)
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for t, m, s in zip(ten, mean, std):
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t.mul_(s).add_(m)
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return torch.clamp(ten, 0, 1).permute(3, 0, 1, 2)
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def get_mask_from_lengths(lengths, max_len=None):
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if max_len is None:
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max_len = torch.max(lengths).item()
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ids = torch.arange(0, max_len, out=torch.LongTensor(max_len)).to(lengths.device)
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mask = (ids < lengths.unsqueeze(1)).bool()
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return mask
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def clip_grad_norm(parameters: list, parameter_names: list, max_norm: float, norm_type: float = 2.0) -> torch.Tensor:
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r"""
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Equivalent to torch.nn.utils.clip_grad_norm_() but with the following changes:
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- Takes in a dictionary of parameters (from get_named_parameters()) instead of a list of parameters.
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- When NaN or inf norms are encountered, the parameter name is printed.
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- error_if_nonfinite removed.
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Clips gradient norm of an iterable of parameters.
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The norm is computed over all gradients together, as if they were
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concatenated into a single vector. Gradients are modified in-place.
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Args:
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parameters (Iterable[Tensor] or Tensor): an iterable of Tensors or a
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single Tensor that will have gradients normalized
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max_norm (float or int): max norm of the gradients
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norm_type (float or int): type of the used p-norm. Can be ``'inf'`` for
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infinity norm.
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error_if_nonfinite (bool): if True, an error is thrown if the total
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norm of the gradients from :attr:``parameters`` is ``nan``,
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``inf``, or ``-inf``. Default: False (will switch to True in the future)
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Returns:
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Total norm of the parameters (viewed as a single vector).
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"""
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parameters = [p for p in parameters if p.grad is not None]
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max_norm = float(max_norm)
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norm_type = float(norm_type)
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if len(parameters) == 0:
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return torch.tensor(0.)
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device = parameters[0].grad.device
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if norm_type == inf:
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norms = [p.grad.detach().abs().max().to(device) for p in parameters]
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total_norm = norms[0] if len(norms) == 1 else torch.max(torch.stack(norms))
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else:
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total_norm = torch.norm(torch.stack([torch.norm(p.grad.detach(), norm_type).to(device) for p in parameters]), norm_type)
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clip_coef = max_norm / (total_norm + 1e-6)
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if clip_coef < 1:
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for p in parameters:
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p.grad.detach().mul_(clip_coef.to(p.grad.device))
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return total_norm
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Loader, Dumper = OrderedYaml()
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|
def load_model_from_config(cfg_file=None, model_name=None, also_load_savepoint=True, load_path=None,
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|
preloaded_options=None, strict_load=True, device=None):
|
|
if preloaded_options is not None:
|
|
opt = preloaded_options
|
|
else:
|
|
with open(cfg_file, mode='r') as f:
|
|
opt = yaml.load(f, Loader=Loader)
|
|
if model_name is None:
|
|
model_cfg = opt['networks'].values()
|
|
model_name = next(opt['networks'].keys())
|
|
else:
|
|
model_cfg = opt['networks'][model_name]
|
|
if 'which_model_G' in model_cfg.keys() and 'which_model' not in model_cfg.keys():
|
|
model_cfg['which_model'] = model_cfg['which_model_G']
|
|
model = networks.create_model(opt, model_cfg).to(device)
|
|
if also_load_savepoint and f'pretrain_model_{model_name}' in opt['path'].keys():
|
|
assert load_path is None
|
|
load_path = opt['path'][f'pretrain_model_{model_name}']
|
|
if load_path is not None:
|
|
print(f"Loading from {load_path}")
|
|
sd = torch.load(load_path, map_location=device)
|
|
model.load_state_dict(sd, strict=strict_load)
|
|
return model
|
|
|
|
|
|
# Mapper for torch.load() that maps cuda devices to the correct CUDA device, but leaves CPU devices alone.
|
|
def map_cuda_to_correct_device(storage, loc):
|
|
if str(loc).startswith('cuda'):
|
|
return storage.cuda(torch.cuda.current_device())
|
|
else:
|
|
return storage.cpu()
|
|
|
|
|
|
def ceil_multiple(base, multiple):
|
|
"""
|
|
Returns the next closest multiple >= base.
|
|
"""
|
|
res = base % multiple
|
|
if res == 0:
|
|
return base
|
|
return base + (multiple - res)
|