| import argparse
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| import os
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|
|
| import numpy as np
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| import pandas as pd
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| import torch
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| from tqdm import tqdm
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|
|
| from model.CPICANN import CPICANN
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|
|
|
|
| def getAnnoMap():
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| vs = pd.read_csv(args.anno_struc).values
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| annos, elems = {}, {}
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| for v in vs:
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| annos[v[1]] = v
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| elems[v[1]] = set(v[3].split(' '))
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|
|
| return annos, elems
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|
|
|
|
| def filter_by_elem(logits, elemMap, elem):
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| for i, e in elemMap.items():
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| if not e <= elem:
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| logits[:, i] = -10 ** 9
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|
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| return logits
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|
|
|
|
| def main():
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| annoMap, elemMap = getAnnoMap()
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|
|
| model = CPICANN(embed_dim=128, num_classes=args.num_classes)
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|
|
| loaded = torch.load(args.load_path)
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| model.load_state_dict(loaded['model'])
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| model.to(args.device)
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| model.eval()
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| print('loaded model from {}'.format(args.load_path))
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| print(model)
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|
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| if args.elem_filtration:
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| print('elem_filtration activated!')
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| else:
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| print('elem_filtration deactivated!')
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|
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| lst = pd.read_csv(args.anno_val).values
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|
|
| top10Hits = np.array([0] * 10, dtype=np.int32)
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|
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| dataLen = len(lst)
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| pbar = tqdm(range(args.infTimes))
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| for i in range(args.infTimes):
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| while True:
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| c1, c2 = np.random.randint(0, dataLen, 2)
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| anno1, anno2 = lst[c1], lst[c2]
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| if anno1[6] != anno2[6]:
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| break
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|
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| data1 = pd.read_csv(os.path.join(args.data_dir, f'{lst[c1][0]}.csv')).values
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| data2 = pd.read_csv(os.path.join(args.data_dir, f'{lst[c2][0]}.csv')).values
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|
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| mixRate1 = np.random.randint(20, 81)
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| mixRate2 = 100 - mixRate1
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|
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| data = mixRate1 * data1 + mixRate2 * data2
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| elem = set(lst[c2][3].strip().split(' ')) | set(lst[c1][3].strip().split(' '))
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|
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| def runFile(v):
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| min_i, scale = min(v), max(v) - min(v)
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| v = (v - min_i) / scale * 100
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|
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| v = torch.tensor(v, dtype=torch.float32).reshape(1, 1, -1)
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| v = v.to(args.device)
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| with torch.no_grad():
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| logits = model(v)
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|
|
|
|
| if args.elem_filtration:
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| logits = filter_by_elem(logits, elemMap, elem)
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|
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| _pred = torch.nn.functional.softmax(logits.squeeze(), dim=0)
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| return _pred.topk(10)
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|
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| top10 = runFile(data)
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|
|
| m = [0] * 10
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| for no, (indice, rate) in enumerate(zip(top10.indices, top10.values)):
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| pred = annoMap[top10.indices[no].item()]
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|
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| if pred[0] == int(anno1[0][:7]):
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| m[no] = 1
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| elif pred[0] == int(anno2[0][:7]):
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| m[no] = 2
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|
|
| if 1 in m[:2] and 2 in m[:2]:
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| top10Hits[1:] += 1
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| elif 1 in m[:3] and 2 in m[:3]:
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| top10Hits[2:] += 1
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| elif 1 in m[:4] and 2 in m[:4]:
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| top10Hits[3:] += 1
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| elif 1 in m[:5] and 2 in m[:5]:
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| top10Hits[4:] += 1
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| elif 1 in m[:6] and 2 in m[:6]:
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| top10Hits[5:] += 1
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| elif 1 in m[:7] and 2 in m[:7]:
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| top10Hits[6:] += 1
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| elif 1 in m[:8] and 2 in m[:8]:
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| top10Hits[7:] += 1
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| elif 1 in m[:9] and 2 in m[:9]:
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| top10Hits[8:] += 1
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| elif 1 in m[:10] and 2 in m[:10]:
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| top10Hits[9:] += 1
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|
|
| pbar.update(1)
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| pbar.close()
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|
|
| for i in range(1, 10):
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| print('top{}Hits: {}%'.format(i + 1, round(top10Hits[i] / args.infTimes * 100, 2)))
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|
|
|
|
| if __name__ == '__main__':
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| parser = argparse.ArgumentParser()
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|
|
| parser.add_argument('--device', default='cuda:0', type=str)
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| parser.add_argument('--data_dir', default='data/val/', type=str)
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| parser.add_argument('--infTimes', default=1000, type=int, help='number of mixed pattern to be inferenced')
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| parser.add_argument('--load_path', default='pretrained/bi-phase_checkpoint_2000.pth', type=str,
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| help='path to load pretrained single-phase identification model')
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| parser.add_argument('--anno_struc', default='annotation/anno_struc.csv', type=str,
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| help='path to annotation file for training data')
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| parser.add_argument('--anno_val', default='annotation/anno_val.csv', type=str,
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| help='path to annotation file for validation data')
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| parser.add_argument('--num_classes', default=23073, type=int, metavar='N')
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|
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| parser.add_argument('--elem_filtration', default=False, type=bool)
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|
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| args = parser.parse_args()
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|
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| main()
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| print('THE END')
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|
|
