-
Notifications
You must be signed in to change notification settings - Fork 0
/
predict.py
52 lines (44 loc) · 1.63 KB
/
predict.py
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
import matplotlib.pyplot as plt
import numpy as np
import torch
from torch import nn
from torch import tensor
from torch import optim
import torch.nn.functional as F
from torch.autograd import Variable
from torchvision import datasets, transforms
import torchvision.models as models
from collections import OrderedDict
import json
import PIL
from PIL import Image
import argparse
import train
ap = argparse.ArgumentParser(description='Predict.py')
ap.add_argument('--input', default='./flowers/test/1/image_06752.jpg', nargs='?', action="store", type = str)
ap.add_argument('--dir', action="store",dest="data_dir", default="./flowers/")
ap.add_argument('--checkpoint', default='checkpoint.pth', nargs='?', action="store", type = str)
ap.add_argument('--top_k', default=5, dest="top_k", action="store", type=int)
ap.add_argument('--category_names', dest="category_names", action="store", default = 'cat_to_name.json')
ap.add_argument('--gpu', default="gpu", action="store", dest="gpu")
pa = ap.parse_args()
path_image = pa.input
top_k = pa.top_k
device = pa.gpu
cat_names = pa.category_names
path = pa.checkpoint
pa = ap.parse_args()
def main():
model=train.load_checkpoint(path)
with open(cat_names, 'r') as json_file:
cat_to_name = json.load(json_file)
probabilities = train.predict(path_image, model, top_k, device)
labels = [cat_to_name[str(index + 1)] for index in np.array(probabilities[1][0])]
probability = np.array(probabilities[0][0])
i=0
while i < top_k:
print("{} it`s probability {}".format(labels[i], probability[i]))
i += 1
print("predect is done!")
if __name__== "__main__":
main()