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436df5c | 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 | from SCHP import networks
from SCHP.utils.transforms import get_affine_transform, transform_logits
from collections import OrderedDict
import torch
import numpy as np
import cv2
from PIL import Image
from torchvision import transforms
def get_palette(num_cls):
""" Returns the color map for visualizing the segmentation mask.
Args:
num_cls: Number of classes
Returns:
The color map
"""
n = num_cls
palette = [0] * (n * 3)
for j in range(0, n):
lab = j
palette[j * 3 + 0] = 0
palette[j * 3 + 1] = 0
palette[j * 3 + 2] = 0
i = 0
while lab:
palette[j * 3 + 0] |= (((lab >> 0) & 1) << (7 - i))
palette[j * 3 + 1] |= (((lab >> 1) & 1) << (7 - i))
palette[j * 3 + 2] |= (((lab >> 2) & 1) << (7 - i))
i += 1
lab >>= 3
return palette
dataset_settings = {
'lip': {
'input_size': [473, 473],
'num_classes': 20,
'label': ['Background', 'Hat', 'Hair', 'Glove', 'Sunglasses', 'Upper-clothes', 'Dress', 'Coat',
'Socks', 'Pants', 'Jumpsuits', 'Scarf', 'Skirt', 'Face', 'Left-arm', 'Right-arm',
'Left-leg', 'Right-leg', 'Left-shoe', 'Right-shoe']
},
'atr': {
'input_size': [512, 512],
'num_classes': 18,
'label': ['Background', 'Hat', 'Hair', 'Sunglasses', 'Upper-clothes', 'Skirt', 'Pants', 'Dress', 'Belt',
'Left-shoe', 'Right-shoe', 'Face', 'Left-leg', 'Right-leg', 'Left-arm', 'Right-arm', 'Bag', 'Scarf']
},
'pascal': {
'input_size': [512, 512],
'num_classes': 7,
'label': ['Background', 'Head', 'Torso', 'Upper Arms', 'Lower Arms', 'Upper Legs', 'Lower Legs'],
}
}
class SCHP:
def __init__(self, ckpt_path, device):
dataset_type = None
if 'lip' in ckpt_path:
dataset_type = 'lip'
elif 'atr' in ckpt_path:
dataset_type = 'atr'
elif 'pascal' in ckpt_path:
dataset_type = 'pascal'
assert dataset_type is not None, 'Dataset type not found in checkpoint path'
self.device = device
self.num_classes = dataset_settings[dataset_type]['num_classes']
self.input_size = dataset_settings[dataset_type]['input_size']
self.aspect_ratio = self.input_size[1] * 1.0 / self.input_size[0]
self.palette = get_palette(self.num_classes)
self.label = dataset_settings[dataset_type]['label']
self.model = networks.init_model('resnet101', num_classes=self.num_classes, pretrained=None).to(device)
self.load_ckpt(ckpt_path)
self.model.eval()
self.transform = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize(mean=[0.406, 0.456, 0.485], std=[0.225, 0.224, 0.229])
])
self.upsample = torch.nn.Upsample(size=self.input_size, mode='bilinear', align_corners=True)
def load_ckpt(self, ckpt_path):
rename_map = {
"decoder.conv3.2.weight": "decoder.conv3.3.weight",
"decoder.conv3.3.weight": "decoder.conv3.4.weight",
"decoder.conv3.3.bias": "decoder.conv3.4.bias",
"decoder.conv3.3.running_mean": "decoder.conv3.4.running_mean",
"decoder.conv3.3.running_var": "decoder.conv3.4.running_var",
"fushion.3.weight": "fushion.4.weight",
"fushion.3.bias": "fushion.4.bias",
}
state_dict = torch.load(ckpt_path, map_location='cpu', weights_only=True)['state_dict']
new_state_dict = OrderedDict()
for k, v in state_dict.items():
name = k[7:] # remove `module.`
new_state_dict[name] = v
new_state_dict_ = OrderedDict()
for k, v in list(new_state_dict.items()):
if k in rename_map:
new_state_dict_[rename_map[k]] = v
else:
new_state_dict_[k] = v
self.model.load_state_dict(new_state_dict_, strict=False)
def _box2cs(self, box):
x, y, w, h = box[:4]
return self._xywh2cs(x, y, w, h)
def _xywh2cs(self, x, y, w, h):
center = np.zeros((2), dtype=np.float32)
center[0] = x + w * 0.5
center[1] = y + h * 0.5
if w > self.aspect_ratio * h:
h = w * 1.0 / self.aspect_ratio
elif w < self.aspect_ratio * h:
w = h * self.aspect_ratio
scale = np.array([w, h], dtype=np.float32)
return center, scale
def preprocess(self, image):
if isinstance(image, str):
img = cv2.imread(image, cv2.IMREAD_COLOR)
elif isinstance(image, Image.Image):
# to cv2 format
img = np.array(image)
h, w, _ = img.shape
# Get person center and scale
person_center, s = self._box2cs([0, 0, w - 1, h - 1])
r = 0
trans = get_affine_transform(person_center, s, r, self.input_size)
input = cv2.warpAffine(
img,
trans,
(int(self.input_size[1]), int(self.input_size[0])),
flags=cv2.INTER_LINEAR,
borderMode=cv2.BORDER_CONSTANT,
borderValue=(0, 0, 0))
input = self.transform(input).to(self.device).unsqueeze(0)
meta = {
'center': person_center,
'height': h,
'width': w,
'scale': s,
'rotation': r
}
return input, meta
def __call__(self, image_or_path):
if isinstance(image_or_path, list):
image_list = []
meta_list = []
for image in image_or_path:
image, meta = self.preprocess(image)
image_list.append(image)
meta_list.append(meta)
image = torch.cat(image_list, dim=0)
else:
image, meta = self.preprocess(image_or_path)
meta_list = [meta]
output = self.model(image)
# upsample_outputs = self.upsample(output[0][-1])
upsample_outputs = self.upsample(output)
upsample_outputs = upsample_outputs.permute(0, 2, 3, 1) # BCHW -> BHWC
output_img_list = []
for upsample_output, meta in zip(upsample_outputs, meta_list):
c, s, w, h = meta['center'], meta['scale'], meta['width'], meta['height']
logits_result = transform_logits(upsample_output.data.cpu().numpy(), c, s, w, h, input_size=self.input_size)
parsing_result = np.argmax(logits_result, axis=2)
output_img = Image.fromarray(np.asarray(parsing_result, dtype=np.uint8))
output_img.putpalette(self.palette)
output_img_list.append(output_img)
return output_img_list[0] if len(output_img_list) == 1 else output_img_list |