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import os
import sys
import matplotlib.pyplot as plt
from pandas.core.common import flatten
import torch
from torch import nn
from torch import optim
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from torch.utils.data import Dataset, DataLoader
from torchvision import datasets, transforms, models
import albumentations as A
from albumentations.pytorch import ToTensorV2
from tqdm import tqdm
import random
import cv2
sys.path.append('/workspace')
import dataset
import argparse
def parse_args():
parser = argparse.ArgumentParser(description='MiSLAS training (Stage-2)')
parser.add_argument('--input',
help='test image path',
required=True,
type=str)
args = parser.parse_args()
return args
classes = ('no_trunk', 'trunk')
test_transforms = A.Compose(
[
A.SmallestMaxSize(max_size=350),
A.CenterCrop(height=256, width=256),
A.Normalize(mean=(0.485, 0.456, 0.406), std=(0.229, 0.224, 0.225)),
ToTensorV2(),
]
)
def main():
args = parse_args()
assert os.path.exists(args.input)
device = torch.device("cuda:3") if torch.cuda.is_available() else torch.device("cpu")
model = models.resnet50(pretrained=True)
model.fc = nn.Sequential(
nn.Dropout(0.5),
nn.Linear(model.fc.in_features, 2)
)
state_dict = torch.load('./result/best_model.pth')
model.load_state_dict(state_dict)
for _, p in model.named_parameters():
p.requires_grad = False
model.to(device)
model.eval()
test_transforms = A.Compose(
[
A.SmallestMaxSize(max_size=350),
A.CenterCrop(height=256, width=256),
A.Normalize(mean=(0.485, 0.456, 0.406), std=(0.229, 0.224, 0.225)),
ToTensorV2(),
]
)
image = cv2.imread(args.input)
image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
image = test_transforms(image=image)["image"]
image = torch.unsqueeze(image, 0).to(device)
output = model(image)
_, preds = output.max(1)
input_cls = 'trunk' if 't_' in args.input else 'no_trunk'
print("input: %s \n" %(input_cls))
print("output: %s" %(classes[preds.item()]))
if __name__ == '__main__':
main() |