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aecb8e5 1db7857 aecb8e5 1db7857 aecb8e5 dd6626e aecb8e5 1db7857 aecb8e5 dd6626e aecb8e5 1db7857 f426032 aecb8e5 f329a69 aecb8e5 5b0f497 1db7857 5b0f497 d6b5da0 d924dcb 64f4e85 1db7857 ec25784 1db7857 232a99d 09e820f 1db7857 f426032 1db7857 f426032 1db7857 f426032 1db7857 f426032 1db7857 f426032 1db7857 f426032 1db7857 f426032 1db7857 f426032 1db7857 aecb8e5 1db7857 aecb8e5 1db7857 aecb8e5 1db7857 aecb8e5 1db7857 f329a69 1db7857 aecb8e5 1db7857 aecb8e5 dd6626e | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 | from PIL import Image
from tqdm import tqdm
from torchvision import transforms
import argparse
import os
from natsort import natsorted
from glob import glob
import torch
import torch.nn as nn
import torch.nn.functional as F
import timm
from tqdm import tqdm
import numpy as np
model = ['swin',
'beit',
'dmnfnet',
'ecaresnet_50',
'efficient',
'regnet',
'vit',
'convnext']
def main(input_model=None):
parser = argparse.ArgumentParser(description='Quick demo Image Classification')
parser.add_argument('--input_dir', default='./test/', type=str, help='Input images root')
parser.add_argument('--result_dir', default='./result/', type=str, help='Results images root')
parser.add_argument('--weights_root', default='experiments/pretrained_models', type=str, help='Weights root')
parser.add_argument('--model', default='convnext', type=str, help='Classifier')
args = parser.parse_args()
args.model = input_model
inp_dir = args.input_dir
out_dir = args.result_dir
os.makedirs(out_dir, exist_ok=True)
files = natsorted(glob(os.path.join(inp_dir, '*.jpg')) + glob(os.path.join(inp_dir, '*.png')))
model,img_size = build_model(args.model, False, args.weights_root)
print('Start predicting......')
result = []
for i, file_ in enumerate(tqdm(files)):
image_name = os.path.split(file_)[-1]
img = Image.open(file_).convert('RGB')
input_ = transform_size(img_size )(img).unsqueeze(0)
with torch.no_grad():
predict_result = model(input_)
# prob = torch.argmax(predict_result, dim=1).item()
top5 = torch.topk(predict_result, 5).indices.tolist()
result = {}
predict_result = predict_result.tolist()
for i in top5[0]:
label = int(i) # class number
prob = predict_result[0][i]
result[label] = prob
print('result:', result)
return result
def transform_size(size: int):
# mean & std for different sizes
mean = {224: (0.5446, 0.4137, 0.3847),
256: (0.5364, 0.4142, 0.3821),
320: (0.5188, 0.4166, 0.3773),
352: (0.5100, 0.4183, 0.3750),
384: (0.5015, 0.4198, 0.3728),
480: (0.4806, 0.4232, 0.3675)}
std = {224: (0.2329, 0.2484, 0.2500),
256: (0.2354, 0.2470, 0.2490),
320: (0.2403, 0.2442, 0.2479),
352: (0.2423, 0.2431, 0.2479),
384: (0.2440, 0.2424, 0.2481),
480: (0.2478, 0.2423, 0.2500)}
transform = transforms.Compose([
transforms.ToTensor(),
transforms.Resize(size=480, interpolation=3),
transforms.CenterCrop(size),
transforms.Normalize(mean=mean[size], std=std[size])
])
return transform
def load_checkpoint(model, weights):
checkpoint = torch.load(weights, map_location=torch.device('cpu'))
model.load_state_dict(checkpoint)
def build_model(model: str, pretrained: bool, pretrained_path: str):
models = ['vit', 'beit', 'swin', 'convnext', 'ecaresnet50', 'dmnfnet', 'regnet', 'efficient']
if model == 'vit':
classifier = vit_model(pretrained=pretrained)
load_checkpoint(classifier, os.path.join(pretrained_path, model+'.pth'))
return classifier.eval(), 384
elif model == 'beit':
classifier = beit_model(pretrained=pretrained)
load_checkpoint(classifier, os.path.join(pretrained_path, model+'_1.pth'))
return classifier.eval(), 384
elif model == 'swin':
classifier = swin_model(pretrained=pretrained)
load_checkpoint(classifier, os.path.join(pretrained_path, model+'.pth'))
return classifier.eval(), 384
elif model == 'convnext':
classifier = convnext_model(pretrained=pretrained)
load_checkpoint(classifier, os.path.join(pretrained_path, model+'.pth'))
return classifier.eval(), 384
elif model == 'ecaresnet_50':
classifier = ecaresnet50_model(pretrained=pretrained)
load_checkpoint(classifier, os.path.join(pretrained_path, model+'.pth'))
return classifier.eval(), 320
elif model == 'dmnfnet':
classifier = dmnfnet_model(pretrained=pretrained)
load_checkpoint(classifier, os.path.join(pretrained_path, model+'.pth'))
return classifier.eval(), 256
elif model == 'regnet':
classifier = regnetz_model(pretrained=pretrained)
load_checkpoint(classifier, os.path.join(pretrained_path, model+'.pth'))
return classifier.eval(), 320
elif model == 'efficient':
classifier = efficientnet_model(pretrained=pretrained)
load_checkpoint(classifier, os.path.join(pretrained_path, model+'.pth'))
return classifier.eval(), 480
else:
raise Exception(
"\nNo corresponding model! \nPlease enter the supported model: \n\n{}".format('\n'.join(models)))
def save_img(filepath, img):
cv2.imwrite(filepath, cv2.cvtColor(img, cv2.COLOR_RGB2BGR))
def clean_folder(folder):
for filename in os.listdir(folder):
file_path = os.path.join(folder, filename)
try:
if os.path.isfile(file_path) or os.path.islink(file_path):
os.unlink(file_path)
elif os.path.isdir(file_path):
shutil.rmtree(file_path)
except Exception as e:
print('Failed to delete %s. Reason: %s' % (file_path, e))
class beit_model(nn.Module):
def __init__(self, classes=219, pretrained=True):
super(beit_model, self).__init__()
self.model = timm.create_model('beit_base_patch16_384', pretrained=pretrained, num_classes=classes)
def forward(self, x):
return self.model(x)
class convnext_model(nn.Module):
def __init__(self, classes=219, pretrained=True):
super(convnext_model, self).__init__()
self.model = timm.create_model('convnext_base_384_in22ft1k', pretrained=pretrained, num_classes=classes)
def forward(self, x):
return self.model(x)
class swin_model(nn.Module):
def __init__(self, classes=219, pretrained=True):
super(swin_model, self).__init__()
self.model = timm.create_model('swin_base_patch4_window12_384', pretrained=pretrained, num_classes=classes)
def forward(self, x):
return self.model(x)
class vit_model(nn.Module):
def __init__(self, classes=219, pretrained=True):
super(vit_model, self).__init__()
self.model = timm.create_model('vit_base_patch16_384', pretrained=pretrained, num_classes=classes)
def forward(self, x):
return self.model(x)
class resmlp_model(nn.Module):
def __init__(self, classes=219, pretrained=True):
super(resmlp_model, self).__init__()
self.model = timm.create_model('resmlp_big_24_224_in22ft1k', pretrained=pretrained, num_classes=classes)
class xcittiny_model(nn.Module):
def __init__(self, classes=219, pretrained=True):
super(xcittiny_model, self).__init__()
self.model = timm.create_model('xcit_tiny_12_p8_384_dist', pretrained=pretrained, num_classes=classes)
def forward(self, x):
return self.model(x)
class ecaresnet269_model(nn.Module):
def __init__(self, classes=219, pretrained=True):
super(ecaresnet269_model, self).__init__()
self.model = timm.create_model('ecaresnet269d', pretrained=pretrained, num_classes=classes)
def forward(self, x):
return self.model(x)
class dmnfnet_model(nn.Module):
def __init__(self, classes=219, pretrained=True):
super(dmnfnet_model, self).__init__()
self.model = timm.create_model('dm_nfnet_f0', pretrained=pretrained, num_classes=classes)
def forward(self, x):
return self.model(x)
class ecaresnet50_model(nn.Module):
def __init__(self, classes=219, pretrained=True):
super(ecaresnet50_model, self).__init__()
self.model = timm.create_model('ecaresnet50t', pretrained=pretrained, num_classes=classes)
def forward(self, x):
return self.model(x)
class regnetz_model(nn.Module):
def __init__(self, classes=219, pretrained=True):
super(regnetz_model, self).__init__()
self.model = timm.create_model('regnetz_e8', pretrained=pretrained, num_classes=classes)
def forward(self, x):
return self.model(x)
class efficientnet_model(nn.Module):
def __init__(self, classes=219, pretrained=True):
super(efficientnet_model, self).__init__()
self.model = timm.create_model('tf_efficientnetv2_m_in21ft1k', pretrained=pretrained, num_classes=classes)
def forward(self, x):
return self.model(x)
def transform_size(size: int):
# mean & std for different sizes
mean = {224: (0.5446, 0.4137, 0.3847),
256: (0.5364, 0.4142, 0.3821),
320: (0.5188, 0.4166, 0.3773),
352: (0.5100, 0.4183, 0.3750),
384: (0.5015, 0.4198, 0.3728),
480: (0.4806, 0.4232, 0.3675)}
std = {224: (0.2329, 0.2484, 0.2500),
256: (0.2354, 0.2470, 0.2490),
320: (0.2403, 0.2442, 0.2479),
352: (0.2423, 0.2431, 0.2479),
384: (0.2440, 0.2424, 0.2481),
480: (0.2478, 0.2423, 0.2500)}
transform = transforms.Compose([
transforms.ToTensor(),
transforms.Resize(size=480, interpolation=3),
transforms.CenterCrop(size),
transforms.Normalize(mean=mean[size], std=std[size])
])
return transform
def build_ensemble_model(model: dict, pretrained: bool):
"""
Args:
model:
{
CLASSIFIER1: ['vit', 384, 'pretrained/vit_testmodel.pth'],
CLASSIFIER2: ['beit', 384, 'pretrained/beit_testmodel.pth'],
CLASSIFIER3: ['swin', 384, 'pretrained/swin_testmodel.pth'],
CLASSIFIER4: ['convnext', 384, 'pretrained/convnext_fold1_best_acc.pth']
}
Returns: [[finish loading pretrained model, corresponding transform], ...]
"""
print('==> Build and load the ensemble models')
ensemble_model = []
for i, key in enumerate(tqdm(model)):
#print(model[key][0])
value = model[key]
each_model = build_model(model=value[0], pretrained=pretrained)
load_checkpoint(each_model, value[2])
each_model.eval()
ensemble_model.append([each_model, transform_size(value[1]), value[1]])
return ensemble_model
if __name__ == '__main__':
main(model=None) |