Spaces:
Runtime error
Runtime error
File size: 8,298 Bytes
fe56845 ecc1dc2 fe56845 |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 |
import numpy as np
import torch
import torch.nn.functional as F
import imageio
import os
from skimage.draw import disk as circle #circle
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):
image = self.visualizer.visualize(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, discriminator=None, kp_detector=None,
optimizer_generator=None, optimizer_discriminator=None, optimizer_kp_detector=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 discriminator is not None:
try:
discriminator.load_state_dict(checkpoint['discriminator'])
except:
print ('No discriminator in the state-dict. Dicriminator will be randomly initialized')
if optimizer_generator is not None:
optimizer_generator.load_state_dict(checkpoint['optimizer_generator'])
if optimizer_discriminator is not None:
try:
optimizer_discriminator.load_state_dict(checkpoint['optimizer_discriminator'])
except RuntimeError as e:
print ('No 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'])
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 visualize(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))
# Equivariance visualization
if 'transformed_frame' in out:
transformed = out['transformed_frame'].data.cpu().numpy()
transformed = np.transpose(transformed, [0, 2, 3, 1])
transformed_kp = out['transformed_kp']['value'].data.cpu().numpy()
images.append((transformed, transformed_kp))
# 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
|