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test_network.py
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test_network.py
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from image_eval import *
from logo_nn import *
from torchvision import datasets
from torchvision import transforms
from torch.utils.data import Dataset, DataLoader
import torch.utils.data as d
from tqdm import tqdm, trange
from matplotlib import pyplot as plt
from run import *
import os
import PIL
view = iter(train_loader)
view = view.next()
image = view[0][0]
label = view[1][0]
def imshow(img):
img = img / 2 + 0.5 # unnormalize
npimg = img.numpy()
plt.imshow(np.transpose(npimg, (1, 2, 0)))
plt.title("This has a label of {}".format(label))
plt.show()
#loading network to specifc epoch to test accuracy
dev = torch.device("cuda" if torch.cuda.is_available() else "cpu")
logoNet = Net().to(dev)
checkpoint = torch.load("/home/antonio/Desktop/chkpnts/model_chkpnt_epoch_83_.tar", map_location = "cpu")
logoNet.load_state_dict(checkpoint['model_state_dict'])
logoNet.eval()
#seeting up dir for trill images and transforming them appropriately
image_path = "/home/antonio/Desktop/trill/trill_logos/logo"
files = [os.path.join(image_path, x) for x in os.listdir(image_path)]
num_logos = len(files)
trill_logos = []
trans = [transforms.ToPILImage(), transforms.Pad(10),
transforms.Resize((128, 128)), transforms.ToTensor(), transforms.Normalize((0.5,0.5,0.5), (0.5, 0.5, 0.5))]
##################################
for i, x in enumerate(files):
image = PIL.Image.open(x)
image = image.convert("RGB")
image = np.asarray(image, dtype=np.float32) / 255
image = image[:, :, :3]
test_im = torch.from_numpy(image)
for x in trans:
test_im = x(test_im)
test_im = test_im.unsqueeze(0)
trill_logos.append(test_im)
for x in trill_logos:
outputs = logoNet(x)
_, predicted = torch.max(outputs, 1)
classes = ["logo", "random"]
print("predicted: {}".format(outputs))
#not_logo = "/home/antonio/Desktop/trill/images/pngs/ABN Newswire (Chinese - Simplified).png"