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import glob
import os
import re
import subprocess
from collections import OrderedDict
import lpips
import numpy as np
import torch
import torch.distributed as dist
from skimage.metrics import peak_signal_noise_ratio as psnr
from skimage.metrics import structural_similarity as ssim
from .matlab_resize import imresize
def reduce_tensors(metrics):
new_metrics = {}
for k, v in metrics.items():
if isinstance(v, torch.Tensor):
dist.all_reduce(v)
v = v / dist.get_world_size()
if type(v) is dict:
v = reduce_tensors(v)
new_metrics[k] = v
return new_metrics
def tensors_to_scalars(tensors):
if isinstance(tensors, torch.Tensor):
tensors = tensors.item()
return tensors
elif isinstance(tensors, dict):
new_tensors = {}
for k, v in tensors.items():
v = tensors_to_scalars(v)
new_tensors[k] = v
return new_tensors
elif isinstance(tensors, list):
return [tensors_to_scalars(v) for v in tensors]
else:
return tensors
def tensors_to_np(tensors):
if isinstance(tensors, dict):
new_np = {}
for k, v in tensors.items():
if isinstance(v, torch.Tensor):
v = v.cpu().numpy()
if type(v) is dict:
v = tensors_to_np(v)
new_np[k] = v
elif isinstance(tensors, list):
new_np = []
for v in tensors:
if isinstance(v, torch.Tensor):
v = v.cpu().numpy()
if type(v) is dict:
v = tensors_to_np(v)
new_np.append(v)
elif isinstance(tensors, torch.Tensor):
v = tensors
if isinstance(v, torch.Tensor):
v = v.cpu().numpy()
if type(v) is dict:
v = tensors_to_np(v)
new_np = v
else:
raise Exception(f'tensors_to_np does not support type {type(tensors)}.')
return new_np
def move_to_cpu(tensors):
ret = {}
for k, v in tensors.items():
if isinstance(v, torch.Tensor):
v = v.cpu()
if type(v) is dict:
v = move_to_cpu(v)
ret[k] = v
return ret
def move_to_cuda(batch, gpu_id=0):
# base case: object can be directly moved using `cuda` or `to`
if callable(getattr(batch, 'cuda', None)):
return batch.cuda(gpu_id, non_blocking=True)
elif callable(getattr(batch, 'to', None)):
return batch.to(torch.device('cuda', gpu_id), non_blocking=True)
elif isinstance(batch, list):
for i, x in enumerate(batch):
batch[i] = move_to_cuda(x, gpu_id)
return batch
elif isinstance(batch, tuple):
batch = list(batch)
for i, x in enumerate(batch):
batch[i] = move_to_cuda(x, gpu_id)
return tuple(batch)
elif isinstance(batch, dict):
for k, v in batch.items():
batch[k] = move_to_cuda(v, gpu_id)
return batch
return batch
def get_last_checkpoint(work_dir, steps=None):
checkpoint = None
last_ckpt_path = None
ckpt_paths = get_all_ckpts(work_dir, steps)
if len(ckpt_paths) > 0:
last_ckpt_path = ckpt_paths[0]
checkpoint = torch.load(last_ckpt_path, map_location='cpu')
return checkpoint, last_ckpt_path
def get_all_ckpts(work_dir, steps=None):
if steps is None:
ckpt_path_pattern = f'{work_dir}/model_ckpt_steps_*.ckpt'
else:
ckpt_path_pattern = f'{work_dir}/model_ckpt_steps_{steps}.ckpt'
return sorted(glob.glob(ckpt_path_pattern),
key=lambda x: -int(re.findall('.*steps\_(\d+)\.ckpt', x)[0]))
def load_checkpoint(model, optimizer, work_dir, steps=None):
checkpoint, last_ckpt_path = get_last_checkpoint(work_dir, steps)
print(f'loding check from: {last_ckpt_path}')
if checkpoint is not None:
stat_dict = checkpoint['state_dict']['model']
new_state_dict = OrderedDict()
for k, v in stat_dict.items():
if k[:7] == 'module.':
k = k[7:] # ε»ζ `module.`
new_state_dict[k] = v
model.load_state_dict(new_state_dict)
model.cuda()
optimizer.load_state_dict(checkpoint['optimizer_states'][0])
training_step = checkpoint['global_step']
del checkpoint
torch.cuda.empty_cache()
else:
training_step = 0
model.cuda()
return training_step
def save_checkpoint(model, optimizer, work_dir, global_step, num_ckpt_keep):
ckpt_path = f'{work_dir}/model_ckpt_steps_{global_step}.ckpt'
print(f'Step@{global_step}: saving model to {ckpt_path}')
checkpoint = {'global_step': global_step}
optimizer_states = []
optimizer_states.append(optimizer.state_dict())
checkpoint['optimizer_states'] = optimizer_states
checkpoint['state_dict'] = {'model': model.state_dict()}
torch.save(checkpoint, ckpt_path, _use_new_zipfile_serialization=False)
for old_ckpt in get_all_ckpts(work_dir)[num_ckpt_keep:]:
remove_file(old_ckpt)
print(f'Delete ckpt: {os.path.basename(old_ckpt)}')
def remove_file(*fns):
for f in fns:
subprocess.check_call(f'rm -rf "{f}"', shell=True)
def plot_img(img):
img = img.data.cpu().numpy()
return np.clip(img, 0, 1)
def load_ckpt(cur_model, ckpt_base_dir, model_name='model', force=True, strict=True):
if os.path.isfile(ckpt_base_dir):
base_dir = os.path.dirname(ckpt_base_dir)
ckpt_path = ckpt_base_dir
checkpoint = torch.load(ckpt_base_dir, map_location='cpu')
else:
base_dir = ckpt_base_dir
checkpoint, ckpt_path = get_last_checkpoint(ckpt_base_dir)
if checkpoint is not None:
state_dict = checkpoint["state_dict"]
if len([k for k in state_dict.keys() if '.' in k]) > 0:
state_dict = {k[len(model_name) + 1:]: v for k, v in state_dict.items()
if k.startswith(f'{model_name}.')}
else:
state_dict = state_dict[model_name]
if not strict:
cur_model_state_dict = cur_model.state_dict()
unmatched_keys = []
for key, param in state_dict.items():
if key in cur_model_state_dict:
new_param = cur_model_state_dict[key]
if new_param.shape != param.shape:
unmatched_keys.append(key)
print("| Unmatched keys: ", key, new_param.shape, param.shape)
for key in unmatched_keys:
del state_dict[key]
cur_model.load_state_dict(state_dict, strict=strict)
print(f"| load '{model_name}' from '{ckpt_path}'.")
else:
e_msg = f"| ckpt not found in {base_dir}."
if force:
assert False, e_msg
else:
print(e_msg)
class Measure:
def __init__(self, net='alex'):
self.model = lpips.LPIPS(net=net)
def measure(self, imgA, imgB, img_lr, sr_scale):
"""
Args:
imgA: [C, H, W] uint8 or torch.FloatTensor [-1,1]
imgB: [C, H, W] uint8 or torch.FloatTensor [-1,1]
img_lr: [C, H, W] uint8 or torch.FloatTensor [-1,1]
sr_scale:
Returns: dict of metrics
"""
if isinstance(imgA, torch.Tensor):
imgA = np.round((imgA.cpu().numpy() + 1) * 127.5).clip(min=0, max=255).astype(np.uint8)
imgB = np.round((imgB.cpu().numpy() + 1) * 127.5).clip(min=0, max=255).astype(np.uint8)
img_lr = np.round((img_lr.cpu().numpy() + 1) * 127.5).clip(min=0, max=255).astype(np.uint8)
imgA = imgA.transpose(1, 2, 0)
imgA_lr = imresize(imgA, 1 / sr_scale)
imgB = imgB.transpose(1, 2, 0)
img_lr = img_lr.transpose(1, 2, 0)
psnr = self.psnr(imgA, imgB)
ssim = self.ssim(imgA, imgB)
lpips = self.lpips(imgA, imgB)
lr_psnr = self.psnr(imgA_lr, img_lr)
res = {'psnr': psnr, 'ssim': ssim, 'lpips': lpips, 'lr_psnr': lr_psnr}
return {k: float(v) for k, v in res.items()}
def lpips(self, imgA, imgB, model=None):
device = next(self.model.parameters()).device
tA = t(imgA).to(device)
tB = t(imgB).to(device)
dist01 = self.model.forward(tA, tB).item()
return dist01
def ssim(self, imgA, imgB):
score, diff = ssim(imgA, imgB, full=True, channel_axis=2, data_range=255)
return score
def psnr(self, imgA, imgB):
return psnr(imgA, imgB, data_range=255)
def t(img):
def to_4d(img):
assert len(img.shape) == 3
img_new = np.expand_dims(img, axis=0)
assert len(img_new.shape) == 4
return img_new
def to_CHW(img):
return np.transpose(img, [2, 0, 1])
def to_tensor(img):
return torch.Tensor(img)
return to_tensor(to_4d(to_CHW(img))) / 127.5 - 1
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