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logger.py
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logger.py
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import numpy as np
import torch
import torch.nn.functional as F
import imageio
import os
from skimage.draw import circle
from modules.util import zip_dimT_to_dimBS, unzip_dimT_from_dimBS
import matplotlib.pyplot as plt
import collections
class Logger:
def __init__(self, log_dir, checkpoint_freq=100, visualizer_params=None, zfill_num=8, log_file_name='log.txt'):
self.loss_list = []
self.cpk_dir = log_dir
self.visualizations_dir = os.path.join(log_dir, 'train-vis')
if not os.path.exists(self.visualizations_dir):
os.makedirs(self.visualizations_dir)
self.log_file = open(os.path.join(log_dir, log_file_name), 'a')
self.zfill_num = zfill_num
self.visualizer = Visualizer(**visualizer_params)
self.checkpoint_freq = checkpoint_freq
self.epoch = 0
self.best_loss = float('inf')
self.names = None
def log_scores(self, loss_names):
loss_mean = np.array(self.loss_list).mean(axis=0)
loss_string = "; ".join(["%s - %.5f" % (name, value) for name, value in zip(loss_names, loss_mean)])
loss_string = str(self.epoch).zfill(self.zfill_num) + ") " + loss_string
print(loss_string, file=self.log_file)
self.loss_list = []
self.log_file.flush()
def visualize_rec(self, inp, out):
inp['source'] = inp['source'].unsqueeze(dim=2).repeat(1, 1, inp['driving'].shape[2], 1, 1)
for k in inp.keys():
if k != 'name':
inp[k] = zip_dimT_to_dimBS(inp[k])
for k in out.keys():
if isinstance(out[k], dict):
for k_s in out[k].keys():
out[k][k_s] = zip_dimT_to_dimBS(out[k][k_s])
else:
out[k] = zip_dimT_to_dimBS(out[k])
image = self.visualizer.visualize_img(inp['driving'], inp['source'], out)
imageio.imsave(os.path.join(self.visualizations_dir, "%s-rec.png" % str(self.epoch).zfill(self.zfill_num)), image)
def save_cpk(self, emergent=False):
cpk = {k: v.state_dict() for k, v in self.models.items()}
cpk['epoch'] = self.epoch
cpk_path = os.path.join(self.cpk_dir, '%s-checkpoint.pth.tar' % str(self.epoch).zfill(self.zfill_num))
if not (os.path.exists(cpk_path) and emergent):
torch.save(cpk, cpk_path)
@staticmethod
def load_cpk(checkpoint_path, generator=None, image_discriminator=None, video_discriminator=None,
kp_detector=None, kp_tracker=None, optimizer_generator=None, optimizer_image_discriminator=None,
optimizer_video_discriminator=None, optimizer_kp_detector=None, optimizer_kp_tracker=None):
checkpoint = torch.load(checkpoint_path)
if generator is not None:
generator.load_state_dict(checkpoint['generator'])
if kp_detector is not None:
kp_detector.load_state_dict(checkpoint['kp_detector'])
if kp_tracker is not None:
kp_tracker.load_state_dict(checkpoint['kp_tracker'])
if image_discriminator is not None:
try:
image_discriminator.load_state_dict(checkpoint['image_discriminator'])
except:
print ('No image_discriminator in the state-dict. ImageDicriminator will be randomly initialized')
if video_discriminator is not None:
try:
video_discriminator.load_state_dict(checkpoint['video_discriminator'])
except:
print ('No video_discriminator in the state-dict. VideoDicriminator will be randomly initialized')
if optimizer_generator is not None:
optimizer_generator.load_state_dict(checkpoint['optimizer_generator'])
if optimizer_image_discriminator is not None:
try:
optimizer_image_discriminator.load_state_dict(checkpoint['optimizer_image_discriminator'])
except RuntimeError as e:
print ('No image discriminator optimizer in the state-dict. Optimizer will be not initialized')
if optimizer_video_discriminator is not None:
try:
optimizer_video_discriminator.load_state_dict(checkpoint['optimizer_video_discriminator'])
except RuntimeError as e:
print ('No video discriminator optimizer in the state-dict. Optimizer will be not initialized')
if optimizer_kp_detector is not None:
optimizer_kp_detector.load_state_dict(checkpoint['optimizer_kp_detector'])
if optimizer_kp_tracker is not None:
optimizer_kp_tracker.load_state_dict(checkpoint['optimizer_kp_tracker'])
return checkpoint['epoch']
def __enter__(self):
return self
def __exit__(self, exc_type, exc_val, exc_tb):
if 'models' in self.__dict__:
self.save_cpk()
self.log_file.close()
def log_iter(self, losses):
losses = collections.OrderedDict(losses.items())
if self.names is None:
self.names = list(losses.keys())
self.loss_list.append(list(losses.values()))
def log_epoch(self, epoch, models, inp, out):
self.epoch = epoch
self.models = models
if (self.epoch + 1) % self.checkpoint_freq == 0:
self.save_cpk()
self.log_scores(self.names)
self.visualize_rec(inp, out)
class Visualizer:
def __init__(self, kp_size=5, draw_border=False, colormap='gist_rainbow'):
self.kp_size = kp_size
self.draw_border = draw_border
self.colormap = plt.get_cmap(colormap)
def draw_image_with_kp(self, image, kp_array):
image = np.copy(image)
spatial_size = np.array(image.shape[:2][::-1])[np.newaxis]
kp_array = spatial_size * (kp_array + 1) / 2
num_kp = kp_array.shape[0]
for kp_ind, kp in enumerate(kp_array):
rr, cc = circle(kp[1], kp[0], self.kp_size, shape=image.shape[:2])
image[rr, cc] = np.array(self.colormap(kp_ind / num_kp))[:3]
return image
def create_image_column_with_kp(self, images, kp):
image_array = np.array([self.draw_image_with_kp(v, k) for v, k in zip(images, kp)])
return self.create_image_column(image_array)
def create_image_column(self, images):
if self.draw_border:
images = np.copy(images)
images[:, :, [0, -1]] = (1, 1, 1)
images[:, :, [0, -1]] = (1, 1, 1)
return np.concatenate(list(images), axis=0)
def create_image_grid(self, *args):
out = []
for arg in args:
if type(arg) == tuple:
out.append(self.create_image_column_with_kp(arg[0], arg[1]))
else:
out.append(self.create_image_column(arg))
return np.concatenate(out, axis=1)
def deform_input(self, inp, deformation):
_, h_old, w_old, _ = deformation.shape
_, _, h, w = inp.shape
if h_old != h or w_old != w:
deformation = deformation.permute(0, 3, 1, 2)
deformation = F.interpolate(deformation, size=(h, w), mode='bilinear')
deformation = deformation.permute(0, 2, 3, 1)
return F.grid_sample(inp, deformation)
def visualize_img(self, driving, source, out):
images = []
# Source image with keypoints
source = source.data.cpu()
kp_source = out['kp_source']['value'].data.cpu().numpy()
source = np.transpose(source, [0, 2, 3, 1])
images.append((source, kp_source))
# Driving image with keypoints
kp_driving = out['kp_driving']['value'].data.cpu().numpy()
driving = driving.data.cpu().numpy()
driving = np.transpose(driving, [0, 2, 3, 1])
images.append((driving, kp_driving))
# Deformed image
if 'deformed' in out:
deformed = out['deformed'].data.cpu().numpy()
deformed = np.transpose(deformed, [0, 2, 3, 1])
images.append(deformed)
# Result with and without keypoints
prediction = out['prediction'].data.cpu().numpy()
prediction = np.transpose(prediction, [0, 2, 3, 1])
if 'kp_norm' in out:
kp_norm = out['kp_norm']['value'].data.cpu().numpy()
images.append((prediction, kp_norm))
images.append(prediction)
# Occlusion map
if 'occlusion_map' in out:
occlusion_map = out['occlusion_map'].data.cpu().repeat(1, 3, 1, 1)
occlusion_map = F.interpolate(occlusion_map, size=source.shape[1:3]).numpy()
occlusion_map = np.transpose(occlusion_map, [0, 2, 3, 1])
images.append(occlusion_map)
# Deformed images according to each individual transform
if 'sparse_deformed' in out:
full_mask = []
for i in range(out['sparse_deformed'].shape[1]):
image = out['sparse_deformed'][:, i].data.cpu()
image = F.interpolate(image, size=source.shape[1:3])
mask = out['mask'][:, i:(i+1)].data.cpu().repeat(1, 3, 1, 1)
mask = F.interpolate(mask, size=source.shape[1:3])
image = np.transpose(image.numpy(), (0, 2, 3, 1))
mask = np.transpose(mask.numpy(), (0, 2, 3, 1))
if i != 0:
color = np.array(self.colormap((i - 1) / (out['sparse_deformed'].shape[1] - 1)))[:3]
else:
color = np.array((0, 0, 0))
color = color.reshape((1, 1, 1, 3))
images.append(image)
if i != 0:
images.append(mask * color)
else:
images.append(mask)
full_mask.append(mask * color)
images.append(sum(full_mask))
image = self.create_image_grid(*images)
image = (255 * image).astype(np.uint8)
return image
def visualize_vid(self, driving, source, out, with_kp=False):
num_frame = driving.shape[2]
source = source.data.cpu().numpy()
driving = driving.data.cpu().numpy()
prediction = out['prediction'].data.cpu().numpy()
deformed = out['deformed'].data.cpu().numpy()
occlusion_map = out['occlusion_map'].data.cpu().repeat(1, 3, 1, 1, 1)
if deformed.shape[3] != occlusion_map.shape[3] or deformed.shape[4] != occlusion_map.shape[4]:
occlusion_map = zip_dimT_to_dimBS(occlusion_map)
occlusion_map = F.interpolate(occlusion_map, size=deformed.shape[3:], mode='bilinear')
occlusion_map = unzip_dimT_from_dimBS(num_frame, occlusion_map)
occlusion_map = occlusion_map.numpy()
if with_kp:
kp_source = out['kp_source']['value'].data.cpu().numpy()
source_with_kp = source.transpose([0, 2, 3, 1])
source_with_kp = np.array([self.draw_image_with_kp(v, k) for v, k in zip(source_with_kp, kp_source)])
source_with_kp = source_with_kp.transpose([0, 3, 1, 2])[:, :, np.newaxis].repeat(driving.shape[2], axis=2)
kp_driving = out['kp_driving']['value'].data.cpu().numpy().squeeze().transpose([1, 0, 2])
prediction_with_kp = prediction.squeeze().transpose([1, 2, 3, 0])
prediction_with_kp = np.array([self.draw_image_with_kp(v, k) for v, k in zip(prediction_with_kp, kp_driving)])
prediction_with_kp = prediction_with_kp.transpose([3, 0, 1, 2])[np.newaxis]
driving_with_kp = driving.squeeze().transpose([1, 2, 3, 0])
driving_with_kp = np.array([self.draw_image_with_kp(v, k) for v, k in zip(driving_with_kp, kp_driving)])
driving_with_kp = driving_with_kp.transpose([3, 0, 1, 2])[np.newaxis]
video = np.concatenate([source_with_kp, driving_with_kp, deformed, prediction_with_kp, prediction, occlusion_map], axis=4)
else:
video = np.concatenate([source, driving, deformed, prediction, occlusion_map], axis=4)
video = np.transpose(video, [0, 2, 3, 4, 1])[0]
video = np.where(video < 0, 0, video)
video = (255 * video).astype(np.uint8)
return video