File size: 6,785 Bytes
45099b6 |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 |
from typing import List
import numpy as np
import torch
from fastai.vision.all import *
# from backend.StampRemoval.util import *
__all__ = ["CustomUnetBlock", "CustomDynamicUnet", "UnetInference"]
class CustomUnetBlock(Module):
"""A quasi-UNet block, using `PixelShuffle_ICNR upsampling`."""
@delegates(ConvLayer.__init__)
def __init__(
self,
up_in_c,
x_in_c,
hook,
final_div=True,
blur=False,
act_cls=defaults.activation,
self_attention=False,
init=nn.init.kaiming_normal_,
norm_type=None,
**kwargs,
):
self.hook = hook
self.shuf = PixelShuffle_ICNR(up_in_c, up_in_c // 2, blur=blur, act_cls=act_cls, norm_type=norm_type)
self.bn = BatchNorm(x_in_c)
ni = up_in_c // 2 + x_in_c
# nf = ni if final_div else ni//2
nf = ni // 2 if final_div else ni // 4
self.conv1 = ConvLayer(ni, nf, act_cls=act_cls, norm_type=norm_type, **kwargs)
self.conv2 = ConvLayer(
nf,
nf,
act_cls=act_cls,
norm_type=norm_type,
xtra=SelfAttention(nf) if self_attention else None,
**kwargs,
)
self.relu = act_cls()
apply_init(nn.Sequential(self.conv1, self.conv2), init)
def forward(self, up_in):
s = self.hook.stored
up_out = self.shuf(up_in)
ssh = s.shape[-2:]
if ssh != up_out.shape[-2:]:
up_out = F.interpolate(up_out, s.shape[-2:], mode="nearest")
cat_x = self.relu(torch.cat([up_out, self.bn(s)], dim=1))
return self.conv2(self.conv1(cat_x))
class CustomDynamicUnet(SequentialEx):
"""Create a U-Net from a given architecture."""
def __init__(
self,
encoder,
n_out,
img_size,
blur=False,
blur_final=True,
self_attention=False,
y_range=None,
last_cross=True,
bottle=False,
act_cls=defaults.activation,
init=nn.init.kaiming_normal_,
norm_type=None,
**kwargs,
):
imsize = img_size
sizes = model_sizes(encoder, size=imsize)
sz_chg_idxs = list(reversed(self._get_sz_change_idxs(sizes)))
self.sfs = hook_outputs([encoder[i] for i in sz_chg_idxs], detach=False)
x = dummy_eval(encoder, imsize).detach()
ni = sizes[-1][1]
middle_conv = nn.Sequential(
ConvLayer(ni, ni, act_cls=act_cls, norm_type=norm_type, **kwargs),
ConvLayer(ni, ni, act_cls=act_cls, norm_type=norm_type, **kwargs),
).eval()
x = middle_conv(x)
layers = [encoder, BatchNorm(ni), nn.ReLU(), middle_conv]
for i, idx in enumerate(sz_chg_idxs):
not_final = i != len(sz_chg_idxs) - 1
up_in_c, x_in_c = int(x.shape[1]), int(sizes[idx][1])
do_blur = blur and (not_final or blur_final)
sa = self_attention and (i == len(sz_chg_idxs) - 3)
unet_block = CustomUnetBlock(
up_in_c,
x_in_c,
self.sfs[i],
final_div=not_final,
blur=do_blur,
self_attention=sa,
act_cls=act_cls,
init=init,
norm_type=norm_type,
**kwargs,
).eval()
layers.append(unet_block)
x = unet_block(x)
ni = x.shape[1]
if imsize != sizes[0][-2:]:
layers.append(PixelShuffle_ICNR(ni, act_cls=act_cls, norm_type=norm_type))
layers.append(ResizeToOrig())
if last_cross:
layers.append(MergeLayer(dense=True))
ni += in_channels(encoder)
layers.append(
ResBlock(
1,
ni,
ni // 2 if bottle else ni,
act_cls=act_cls,
norm_type=norm_type,
**kwargs,
)
)
layers += [ConvLayer(ni, n_out, ks=1, act_cls=None, norm_type=norm_type, **kwargs)]
apply_init(nn.Sequential(layers[3], layers[-2]), init)
# apply_init(nn.Sequential(layers[2]), init)
if y_range is not None:
layers.append(SigmoidRange(*y_range))
super().__init__(*layers)
def _get_sz_change_idxs(self, sizes):
"Get the indexes of the layers where the size of the activation changes."
feature_szs = [size[-1] for size in sizes]
sz_chg_idxs = list(np.where(np.array(feature_szs[:-1]) != np.array(feature_szs[1:]))[0])
return sz_chg_idxs
def __del__(self):
if hasattr(self, "sfs"):
self.sfs.remove()
class PerceptualLoss:
pass
class UnetInference:
def __init__(self, model_path):
"""Inference interface for unet model"""
self.learn = load_learner(model_path)
self.learn.model.eval()
def __call__(self, image_array: str, bs: int = 1) -> List[np.ndarray]:
"""Perform forward pass and decode the prediction of Unet model
Args:
image_array (list): list of numpy array
bs (int, optional): [batch size]. Defaults to 1.
Returns:
[list]: list of numpy array
"""
if len(image_array) < 1:
return []
batches = self.__build_batches(image_array, bs=bs)
outs = []
with torch.no_grad():
for b in batches:
outs.append(self.learn.model(b))
del b
pil_images = self.__decode_prediction(outs)
return pil_images
def __decode_prediction(self, preds):
out = []
i2f = IntToFloatTensor()
for pred in preds:
img_np = i2f.decodes(pred.squeeze()).numpy()
img_np = img_np.transpose(1, 2, 0)
img_np = img_np.astype(np.uint8)
out.append(img_np)
# out.append(Image.fromarray(img_np))
del img_np
return out
def __build_batches(self, image_array: list, bs=1):
"Builds batches to skip `DataLoader` overhead"
type_tfms = [PILImage.create]
item_tfms = [ToTensor()]
type_pipe = Pipeline(type_tfms)
item_pipe = Pipeline(item_tfms)
i2f = IntToFloatTensor()
batches = []
batch = []
k = 0
for i, im in enumerate(image_array):
batch.append(item_pipe(type_pipe(im)))
k += 1
if i == len(image_array) - 1 or k == bs:
# batches.append(torch.cat([norm(i2f(b.cuda())) for b in batch]))
batches.append(torch.stack([i2f(b.cpu()) for b in batch], axis=0))
batch = []
k = 0
return batches
|