import torch
from model import MyNet
# Create the model and load the weights
model = MyNet()
model.load_state_dict(torch.load('my_network.pth'))
# Create dummy input
dummy_input = torch.rand(1, 3, 32, 32)
# Define input / output names
input_names = ["my_input"]
output_names = ["my_output"]
# Convert the PyTorch model to ONNX
torch.onnx.export(model,
dummy_input,
"my_network.onnx",
verbose=True,
input_names=input_names,
output_names=output_names)
def save(output, fname, out_dir=args.out_dir):
out_path = os.path.join(dir, fname)
# define model
loadedparams = torch.load(args.model_dir, map_location=args.device)
model = MyNet(3)
if use_cuda:
model.cuda()
loss_fn = torch.nn.CrossEntropyLoss()
optimizer = torch.optim.SGD(model.parameters(), lr=args.lr, momentum=0.9)
label_colours = np.random.randint(255, size=(100, 3))
model.eval()
if args.model_dir != '':
model.load_state_dict(loadedparams['model'], strict=True)
else:
print('PLEASE LOAD A MODEL_DIR AS AN OPTION')
# main function for testing
if __name__ == '__main__':
# process a single image
if args.input != '':
data = readfile(args.input)
with torch.no_grad():
output = model(data)[0]
print(output)
quit()
# visualize predicted output
'Over_ink': 'over_ink',
'Set_off': 'set_off',
'Under_ink': 'under_ink',
'Wiping': 'wiping',
}
class_names = ['dot', 'miss_print', 'over_ink', 'set_off', 'under_ink', 'wiping']
# instantiate the CNN
use_cuda = torch.cuda.is_available()
layer_sizes = [512, 256, 128]
model = MyNet(output_size=6, layer_sizes=layer_sizes)
if use_cuda:
model = model.cuda()
model.load_state_dict(torch.load('model/model_resnet101_512_256_128_back.pt', map_location=torch.device('cpu')))
# ### Get the directory path from the argument
if __name__ == "__main__":
dir_path = sys.argv[1].strip()
#in put the directory path
#dir_path = input('Enter the images directory path: ').strip()
#print(dir_path)
#delete the output folder if exists
for files in os.listdir(dir_path):
if files == 'output':
shutil.rmtree(os.path.join(dir_path, files))
#shutil.rmtree(os.path.join(os.getcwd()+'/' + dir_path, files))
def main(args):
#hyper parameter
end_epoch = 200
lr = 0.001
beta1 = 0.5
beta2 = 0.99
gpu = 0
#set model
model = MyNet()
#set GPU or CPU
if gpu >= 0 and torch.cuda.is_available():
device = 'cuda:{}'.format(gpu)
else:
device = 'cpu'
model.to(device)
model.load_state_dict(
torch.load("models/" + args.model + "/" + str(args.epoch - 1) +
".pth"))
model.eval()
criteria = nn.CrossEntropyLoss()
dataset = MyDataset("data/", is_train=False)
test_loader = torch.utils.data.DataLoader(dataset,
batch_size=1,
shuffle=True)
with torch.no_grad():
epoch_loss = 0
epoch_acc = 0
if not os.path.exists("result/" + args.model + "/true_favo/"):
os.makedirs("result/" + args.model + "/true_favo/")
os.makedirs("result/" + args.model + "/true_no_favo/")
os.makedirs("result/" + args.model + "/false_favo/")
os.makedirs("result/" + args.model + "/false_no_favo/")
for i, data in enumerate(test_loader):
print("\riteration: {}".format(i), end="")
inputs, labels = data
outputs = model(inputs.to(device))
_, preds = torch.max(outputs.data, 1)
loss = criteria(outputs, labels.to(device))
favo_preds = preds.to('cpu')
if favo_preds == labels:
if favo_preds == 1:
save_image(
inputs, "result/" + args.model + "/true_favo/" +
str(i) + ".jpg")
elif favo_preds == 0:
save_image(
inputs, "result/" + args.model + "/true_no_favo/" +
str(i) + ".jpg")
else:
if favo_preds == 1:
save_image(
inputs, "result/" + args.model + "/false_favo/" +
str(i) + ".jpg")
elif favo_preds == 0:
save_image(
inputs, "result/" + args.model + "/false_no_favo/" +
str(i) + ".jpg")
epoch_loss += loss.data.to('cpu') * inputs.size(0)
epoch_acc += torch.sum(favo_preds == labels.data)
epoch_loss /= len(test_loader)
epoch_acc = epoch_acc / float(len(test_loader))
print("[Loss: {}] [Acc: {}]".format(epoch_loss, epoch_acc))
import torch
from model import MyNet
from dataLoader import DataGetter, Euler2Rot
import numpy as np
from plotting import plotXYZ
from tkinter import filedialog as fd
# data_dir = 'D:/data_odometry_gray/dataset'
data_dir = 'D:/data_odometry_color/dataset'
folder_num = 6
batch_size = 1
model_name = fd.askopenfilename()
model = MyNet()
model.load_state_dict(torch.load(model_name))
getter = DataGetter(data_dir,
batch_size,
folder_num,
folder_num,
randomize_data=False)
if torch.cuda.is_available():
device = 'cuda'
model.cuda()
else:
device = 'cpu'
model.eval()
running_R = torch.eye(3, dtype=torch.float)
running_t = torch.zeros((1, 3), dtype=torch.float)