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fix : load_class() path replace on windows #60

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fix : load_class() path replace on windows #60

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d3b96b2
fix: load_class path
v4zha Feb 25, 2024
b289112
knk_vision
v4zha Mar 24, 2024
f9e2d68
vidar import
v4zha Mar 24, 2024
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6 changes: 3 additions & 3 deletions hubconf.py
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import torch
import torch.nn.functional as F

from vidar.core.evaluator import Evaluator
from vidar.utils.config import read_config
from vidar.utils.setup import setup_arch, setup_network
from knk_vision.vidar.vidar.core.evaluator import Evaluator
from knk_vision.vidar.vidar.utils.config import read_config
from knk_vision.vidar.vidar.utils.setup import setup_arch, setup_network


def DeFiNe(pretrained=True, **kwargs):
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6 changes: 3 additions & 3 deletions scripts/launch.py
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import fire
import torch

from vidar.core.trainer import Trainer
from vidar.utils.config import read_config
from vidar.core.wrapper import Wrapper
from knk_vision.vidar.vidar.core.trainer import Trainer
from knk_vision.vidar.vidar.utils.config import read_config
from knk_vision.vidar.vidar.core.wrapper import Wrapper


def train(cfg, **kwargs):
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6 changes: 3 additions & 3 deletions scripts/launch_ddp.py
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import torch.distributed as dist
import torch.multiprocessing as mp

from vidar.core.trainer import Trainer
from vidar.utils.config import read_config
from vidar.core.wrapper import Wrapper
from knk_vision.vidar.vidar.core.trainer import Trainer
from knk_vision.vidar.vidar.utils.config import read_config
from knk_vision.vidar.vidar.core.wrapper import Wrapper


def train(cfg, **kwargs):
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8 changes: 4 additions & 4 deletions scripts/sesc_view.py
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from camviz import Draw
from camviz.objects.camera import Camera as CVCam

from vidar.arch.models.releaseSesc.SescInference import SescInference
from vidar.geometry.camera import Camera
from vidar.geometry.pose_utils import pose_vec2mat_homogeneous
from vidar.utils.viz import viz_depth
from knk_vision.vidar.vidar.arch.models.releaseSesc.SescInference import SescInference
from knk_vision.vidar.vidar.geometry.camera import Camera
from knk_vision.vidar.vidar.geometry.pose_utils import pose_vec2mat_homogeneous
from knk_vision.vidar.vidar.utils.viz import viz_depth

cam_colors = ['red', 'yel', 'gre', 'blu', 'mag', 'cya'] * 100 # Full
camera_scale = 1.
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4 changes: 2 additions & 2 deletions vidar/arch/blocks/depth/SigmoidToInvDepth.py
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import torch.nn as nn

from vidar.utils.decorators import iterate2
from vidar.utils.depth import inv2depth
from knk_vision.vidar.vidar.utils.decorators import iterate2
from knk_vision.vidar.vidar.utils.depth import inv2depth


class SigmoidToInvDepth(nn.Module, ABC):
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2 changes: 1 addition & 1 deletion vidar/arch/blocks/depth/SigmoidToLogDepth.py
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import torch
import torch.nn as nn

from vidar.utils.decorators import iterate2
from knk_vision.vidar.vidar.utils.decorators import iterate2


class SigmoidToLogDepth(nn.Module, ABC):
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6 changes: 3 additions & 3 deletions vidar/arch/blocks/image/ViewSynthesis.py
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import torch
import torch.nn as nn

from vidar.utils.flow import coords_from_optical_flow, coords_from_motion
from vidar.utils.tensor import grid_sample, interpolate
from vidar.utils.types import is_list, is_dict
from knk_vision.vidar.vidar.utils.flow import coords_from_optical_flow, coords_from_motion
from knk_vision.vidar.vidar.utils.tensor import grid_sample, interpolate
from knk_vision.vidar.vidar.utils.types import is_list, is_dict


class ViewSynthesis(nn.Module, ABC):
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6 changes: 3 additions & 3 deletions vidar/arch/blocks/image/ViewSynthesisSelfSup.py
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import torch
import torch.nn as nn

from vidar.utils.flow import coords_from_optical_flow
from vidar.utils.tensor import grid_sample, interpolate
from vidar.utils.types import is_list
from knk_vision.vidar.vidar.utils.flow import coords_from_optical_flow
from knk_vision.vidar.vidar.utils.tensor import grid_sample, interpolate
from knk_vision.vidar.vidar.utils.types import is_list


class ViewSynthesis(nn.Module, ABC):
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4 changes: 2 additions & 2 deletions vidar/arch/losses/BaseLoss.py
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import torch
import torch.nn as nn

from vidar.utils.tensor import same_shape, interpolate
from vidar.utils.types import is_dict, is_list
from knk_vision.vidar.vidar.utils.tensor import same_shape, interpolate
from knk_vision.vidar.vidar.utils.types import is_dict, is_list


class BaseLoss(nn.Module, ABC):
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8 changes: 4 additions & 4 deletions vidar/arch/losses/ConsistencyLoss.py
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import torch

from vidar.arch.losses.BaseLoss import BaseLoss
from vidar.utils.data import get_mask_from_list
from vidar.utils.tensor import interpolate, same_shape, multiply_mask, masked_average
from vidar.utils.types import is_list
from knk_vision.vidar.vidar.arch.losses.BaseLoss import BaseLoss
from knk_vision.vidar.vidar.utils.data import get_mask_from_list
from knk_vision.vidar.vidar.utils.tensor import interpolate, same_shape, multiply_mask, masked_average
from knk_vision.vidar.vidar.utils.types import is_list


class ConsistencyLoss(BaseLoss, ABC):
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10 changes: 5 additions & 5 deletions vidar/arch/losses/MultiCamPhotometricLoss.py
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import torch

from vidar.arch.losses.MultiViewPhotometricLoss import MultiViewPhotometricLoss
from vidar.arch.networks.layers.fsm.utils import coords_from_motion, warp_from_coords, mask_from_coords
from vidar.utils.depth import inv2depth
from vidar.utils.tensor import match_scales
from vidar.utils.types import is_list, is_double_list
from knk_vision.vidar.vidar.arch.losses.MultiViewPhotometricLoss import MultiViewPhotometricLoss
from knk_vision.vidar.vidar.arch.networks.layers.fsm.utils import coords_from_motion, warp_from_coords, mask_from_coords
from knk_vision.vidar.vidar.utils.depth import inv2depth
from knk_vision.vidar.vidar.utils.tensor import match_scales
from knk_vision.vidar.vidar.utils.types import is_list, is_double_list


class MultiCamPhotometricLoss(MultiViewPhotometricLoss):
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8 changes: 4 additions & 4 deletions vidar/arch/losses/MultiViewPhotometricLoss.py
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import torch.nn as nn
import torch.nn.functional as tf

from vidar.arch.losses.BaseLoss import BaseLoss
from vidar.geometry.camera import Camera
from vidar.utils.depth import inv2depth
from vidar.utils.tensor import match_scales
from knk_vision.vidar.vidar.arch.losses.BaseLoss import BaseLoss
from knk_vision.vidar.vidar.geometry.camera import Camera
from knk_vision.vidar.vidar.utils.depth import inv2depth
from knk_vision.vidar.vidar.utils.tensor import match_scales


def view_synthesis(ref_image, depth, ref_cam, cam,
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6 changes: 3 additions & 3 deletions vidar/arch/losses/PhotometricLoss.py
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import torch

from vidar.arch.losses.BaseLoss import BaseLoss
from vidar.arch.losses.SSIMLoss import SSIMLoss
from vidar.utils.tensor import interpolate
from knk_vision.vidar.vidar.arch.losses.BaseLoss import BaseLoss
from knk_vision.vidar.vidar.arch.losses.SSIMLoss import SSIMLoss
from knk_vision.vidar.vidar.utils.tensor import interpolate


class PhotometricLoss(BaseLoss, ABC):
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6 changes: 3 additions & 3 deletions vidar/arch/losses/PoseConsistencyLoss.py
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from torch import nn
from torch import linalg as LA

from vidar.utils.config import cfg_has
from vidar.arch.losses.BaseLoss import BaseLoss
from vidar.geometry.pose_utils import \
from knk_vision.vidar.vidar.utils.config import cfg_has
from knk_vision.vidar.vidar.arch.losses.BaseLoss import BaseLoss
from knk_vision.vidar.vidar.geometry.pose_utils import \
(invert_multi_pose, pose_tensor2euler_tensor, pose_tensor2transl_vec, pose_tensor2rotmatrix, invert_pose)


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10 changes: 5 additions & 5 deletions vidar/arch/losses/ReprojectionLoss.py
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import torch

from vidar.arch.losses.BaseLoss import BaseLoss
from vidar.utils.config import cfg_has
from vidar.utils.data import get_from_list, get_mask_from_list, get_scale_from_dict
from vidar.utils.tensor import interpolate, multiply_args, masked_average
from vidar.utils.types import is_list, is_dict
from knk_vision.vidar.vidar.arch.losses.BaseLoss import BaseLoss
from knk_vision.vidar.vidar.utils.config import cfg_has
from knk_vision.vidar.vidar.utils.data import get_from_list, get_mask_from_list, get_scale_from_dict
from knk_vision.vidar.vidar.utils.tensor import interpolate, multiply_args, masked_average
from knk_vision.vidar.vidar.utils.types import is_list, is_dict


class ReprojectionLoss(BaseLoss, ABC):
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2 changes: 1 addition & 1 deletion vidar/arch/losses/SSIMLoss.py
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import torch
import torch.nn as nn

from vidar.arch.losses.BaseLoss import BaseLoss
from knk_vision.vidar.vidar.arch.losses.BaseLoss import BaseLoss


class SSIMLoss(BaseLoss, ABC):
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6 changes: 3 additions & 3 deletions vidar/arch/losses/SmoothnessLoss.py
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import torch

from vidar.arch.losses.BaseLoss import BaseLoss
from vidar.utils.data import get_mask_from_list
from vidar.utils.tensor import same_shape, interpolate_image
from knk_vision.vidar.vidar.arch.losses.BaseLoss import BaseLoss
from knk_vision.vidar.vidar.utils.data import get_mask_from_list
from knk_vision.vidar.vidar.utils.tensor import same_shape, interpolate_image


class SmoothnessLoss(BaseLoss, ABC):
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6 changes: 3 additions & 3 deletions vidar/arch/losses/SupervisedDepthLoss.py
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import torch
import torch.nn as nn

from vidar.arch.losses.BaseLoss import BaseLoss
from vidar.utils.data import get_mask_from_list, get_from_list
from vidar.utils.depth import depth2index, get_depth_bins, depth2inv, scale_and_shift_pred, align
from knk_vision.vidar.vidar.arch.losses.BaseLoss import BaseLoss
from knk_vision.vidar.vidar.utils.data import get_mask_from_list, get_from_list
from knk_vision.vidar.vidar.utils.depth import depth2index, get_depth_bins, depth2inv, scale_and_shift_pred, align


def grad(x):
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8 changes: 4 additions & 4 deletions vidar/arch/losses/SupervisedImageLoss.py
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import torch.nn as nn

from vidar.arch.losses.BaseLoss import BaseLoss
from vidar.utils.data import get_mask_from_list, get_from_list
from vidar.utils.tensor import same_shape, interpolate
from vidar.utils.types import is_list
from knk_vision.vidar.vidar.arch.losses.BaseLoss import BaseLoss
from knk_vision.vidar.vidar.utils.data import get_mask_from_list, get_from_list
from knk_vision.vidar.vidar.utils.tensor import same_shape, interpolate
from knk_vision.vidar.vidar.utils.types import is_list


class LossWrapper(nn.Module):
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6 changes: 3 additions & 3 deletions vidar/arch/losses/SupervisedNormalsLoss.py
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import torch

from vidar.arch.losses.BaseLoss import BaseLoss
from vidar.utils.data import get_from_list, get_mask_from_list
from vidar.utils.depth import calculate_normals
from knk_vision.vidar.vidar.arch.losses.BaseLoss import BaseLoss
from knk_vision.vidar.vidar.utils.data import get_from_list, get_mask_from_list
from knk_vision.vidar.vidar.utils.depth import calculate_normals


def grad_mask(mask):
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8 changes: 4 additions & 4 deletions vidar/arch/losses/SupervisedOpticalFlowLoss.py
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import torch
import torch.nn as nn

from vidar.arch.losses.BaseLoss import BaseLoss
from vidar.utils.data import get_mask_from_list, get_from_list
from vidar.utils.depth import depth2inv
from knk_vision.vidar.vidar.arch.losses.BaseLoss import BaseLoss
from knk_vision.vidar.vidar.utils.data import get_mask_from_list, get_from_list
from knk_vision.vidar.vidar.utils.depth import depth2inv


def get_criterion(method):
Expand Down Expand Up @@ -65,7 +65,7 @@ def calculate(self, pred, gt, mask=None, soft_mask=None, logvar=None):
mask = self.interp_nearest(mask, gt)
soft_mask = self.interp_bilinear(soft_mask, gt)

from vidar.utils.write import write_image
from knk_vision.vidar.vidar.utils.write import write_image
write_image('mask.png', mask[0])

# Flatten tensors
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6 changes: 3 additions & 3 deletions vidar/arch/losses/SupervisedSemanticLoss.py
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import torch

from vidar.arch.losses.BaseLoss import BaseLoss
from vidar.utils.tensor import same_shape, interpolate
from vidar.utils.types import is_list
from knk_vision.vidar.vidar.arch.losses.BaseLoss import BaseLoss
from knk_vision.vidar.vidar.utils.tensor import same_shape, interpolate
from knk_vision.vidar.vidar.utils.types import is_list


class SupervisedSemanticLoss(BaseLoss, ABC):
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4 changes: 2 additions & 2 deletions vidar/arch/models/BaseModel.py
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import torch
import torch.nn as nn

from vidar.utils.config import cfg_has
from vidar.utils.types import is_dict
from knk_vision.vidar.vidar.utils.config import cfg_has
from knk_vision.vidar.vidar.utils.types import is_dict


class BaseModel(nn.Module):
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14 changes: 7 additions & 7 deletions vidar/arch/models/define/DefineGenericModel.py
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import numpy as np
import torch

from vidar.arch.models.BaseModel import BaseModel
from vidar.arch.models.define.DefineModel import augment_canonical, create_virtual_cameras
from vidar.geometry.camera import Camera
from vidar.geometry.pose import Pose
from vidar.utils.data import flatten
from vidar.utils.types import is_list
from vidar.arch.networks.perceiver.DeFiNeNet import DeFiNeNet
from knk_vision.vidar.vidar.arch.models.BaseModel import BaseModel
from knk_vision.vidar.vidar.arch.models.define.DefineModel import augment_canonical, create_virtual_cameras
from knk_vision.vidar.vidar.geometry.camera import Camera
from knk_vision.vidar.vidar.geometry.pose import Pose
from knk_vision.vidar.vidar.utils.data import flatten
from knk_vision.vidar.vidar.utils.types import is_list
from knk_vision.vidar.vidar.arch.networks.perceiver.DeFiNeNet import DeFiNeNet


def sample_pred_gt(output, gt, pred):
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8 changes: 4 additions & 4 deletions vidar/arch/models/define/DefineInferenceModel.py
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from abc import ABC

from vidar.arch.networks.perceiver.DeFiNeNet import DefineNet
from vidar.arch.models.BaseModel import BaseModel
from vidar.geometry.camera_nerf import CameraNerf
from vidar.geometry.pose import Pose
from knk_vision.vidar.vidar.arch.networks.perceiver.DeFiNeNet import DefineNet
from knk_vision.vidar.vidar.arch.models.BaseModel import BaseModel
from knk_vision.vidar.vidar.geometry.camera_nerf import CameraNerf
from knk_vision.vidar.vidar.geometry.pose import Pose


class DefineInferenceModel(BaseModel, ABC):
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4 changes: 2 additions & 2 deletions vidar/arch/models/define/DefineModel.py
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import torch

from vidar.geometry.pose import Pose
from vidar.utils.types import is_int
from knk_vision.vidar.vidar.geometry.pose import Pose
from knk_vision.vidar.vidar.utils.types import is_int


def augment_canonical(pose):
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16 changes: 8 additions & 8 deletions vidar/arch/models/depth/DepthFormerModel.py
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import torch

from vidar.arch.blocks.image.ViewSynthesis import ViewSynthesis
from vidar.arch.models.BaseModel import BaseModel
from vidar.arch.models.utils import make_rgb_scales, break_context, create_cameras
from vidar.utils.data import get_from_dict
from vidar.utils.depth import inv2depth
from vidar.utils.tensor import interpolate, multiply_args
from vidar.utils.types import is_str, is_tuple, is_list, is_dict
from knk_vision.vidar.vidar.arch.blocks.image.ViewSynthesis import ViewSynthesis
from knk_vision.vidar.vidar.arch.models.BaseModel import BaseModel
from knk_vision.vidar.vidar.arch.models.utils import make_rgb_scales, break_context, create_cameras
from knk_vision.vidar.vidar.utils.data import get_from_dict
from knk_vision.vidar.vidar.utils.depth import inv2depth
from knk_vision.vidar.vidar.utils.tensor import interpolate, multiply_args
from knk_vision.vidar.vidar.utils.types import is_str, is_tuple, is_list, is_dict


def fix_predictions(predictions):
Expand Down Expand Up @@ -339,7 +339,7 @@ def forward(self, batch, epoch=0):
cal = self.networks['multi_depth'].networks['cal']
depth1 = depth_multi[0]
depth2 = predictions['depth_regr'][0][0]
from vidar.utils.tensor import interpolate
from knk_vision.vidar.vidar.utils.tensor import interpolate
depth2 = interpolate(depth2, size=depth1.shape[-2:], scale_factor=None, mode='nearest')
predictions['depth_regr'][0].insert(0, cal(depth1, depth2, rgb))

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2 changes: 1 addition & 1 deletion vidar/arch/models/depth/DepthModel.py
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# Copyright 2023 Toyota Research Institute. All rights reserved.

from vidar.arch.models.BaseModel import BaseModel
from knk_vision.vidar.vidar.arch.models.BaseModel import BaseModel


class DepthModel(BaseModel):
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