Diksha-agg
commented
1 year ago I did this and I got a list of 4 tensors.
import torch from PIL import Image import torchvision.transforms as transforms import matplotlib.pyplot as plt import numpy as np
from vidar.arch.networks.depth.MonoDepthResNet import MonoDepthResNet from vidar.utils.config import read_config
Create network
cfg = read_config('demos/run_network/config.yaml') net = MonoDepthResNet(cfg)
Load and preprocess the image
image_path = "frame0.jpg" # Replace with the path to your image file image = Image.open(image_path).convert("RGB") transform = transforms.Compose([ transforms.Resize((256, 256)), # Resize the image to 256x256 pixels transforms.ToTensor(), # Convert the image to a PyTorch tensor transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)) # Normalize the image ]) input_tensor = transform(image).unsqueeze(0) # Add a batch dimension to the input tensor depth = net(rgb=input_tensor)['depths'] print(depth)
Hope this might help!
`import torch from vidar.core.wrapper import Wrapper from vidar.arch.networks.depth.PackNet import PackNet from vidar.utils.config import read_config import numpy as np import cv2 import torchvision.transforms as transforms from PIL import Image
def resize_image(image, shape, interpolation=Image.ANTIALIAS): transform = transforms.Resize(shape, interpolation=interpolation) return transform(image)
def to_tensor(image, tensor_type='torch.FloatTensor'): transform = transforms.ToTensor() return transform(image).type(tensor_type)
cfg = read_config('configs/papers/packnet/inference_packnet.yaml') net = PackNet(cfg)
rgb = Image.open('/data/vidar/DDAD_images/000081/CAMERA_01/1568648362895305.png') rgb = resize_image(rgb, (608, 968)) rgb = to_tensor(rgb).unsqueeze(0) print(rgb) depth = net(rgb=rgb)['depths'] print(depth) `
I was able to run the demos/run_network/run_network.py without issues but after applying it to the real image, it throws: Traceback (most recent call last): File "demos/run_network/run_network.py", line 48, in
depth = net(rgb=rgb)['depths']
File "/usr/local/lib/python3.6/dist-packages/torch/nn/modules/module.py", line 1102, in _call_impl
return forward_call(*input, *kwargs)
File "/workspace/vidar/vidar/arch/networks/depth/PackNet.py", line 118, in forward
x4p = self.pack4(x4)
File "/usr/local/lib/python3.6/dist-packages/torch/nn/modules/module.py", line 1102, in _call_impl
return forward_call(input, **kwargs)
File "/workspace/vidar/vidar/arch/networks/layers/packnet/packnet.py", line 242, in forward
x = self.pack(x)
File "/workspace/vidar/vidar/arch/networks/layers/packnet/packnet.py", line 150, in packing
x = x.contiguous().view(b, c, out_h, r, out_w, r)
RuntimeError: shape '[1, 256, 38, 2, 60, 2]' is invalid for input of size 2354176