SqrtRyan

import pandas as pd
from rp import *

sys.path.append("/usr/local/google/home/burgert/CleanCode/Management")
import syncutil

dataset_root = "~/CleanCode/Datasets/Envato"
dataset_root = rp.get_absolute_path(dataset_root)

videos_folder_name = "Videos"

SqrtRyan

import numpy as np
import random
import cv2
import rp  # Pip Install RP


class ShapePlugin:
    """Base class for shape plugins."""

    def __init__(self, name):

SqrtRyan

class MyModel(nn.Module):
    def __init__(self):
        super().__init__()
        #############################################################################
        # TODO: Initialize the network weights                                      #
        #############################################################################

        self.conv1 = nn.Conv2d(in_channels=3, out_channels=64, kernel_size=5, stride=1, padding=2)
        self.bn1 = nn.BatchNorm2d(64)
        self.relu = nn.LeakyReLU(0.1)

SqrtRyan

# 2025-02-06 21:17:00.685180
# 2025-02-06 16:18:53.465744
from PIL import Image
from rp import *
import cv2
import numpy as np
import torch
import torchvision.transforms as T

SqrtRyan

# 2025-02-06 21:17:00.685180
# 2025-02-06 16:18:53.465744
from PIL import Image
from rp import *
import cv2
import numpy as np
import torch
import torchvision.transforms as T

SqrtRyan

from rp import *
import numpy as np
import torch
import torchvision.transforms as T
from PIL import Image
import cv2

def approximate_matrix_by_rank(matrix, rank=1):
    """
    Approximates a given matrix by another matrix with a specified rank,

SqrtRyan

import numpy as np
import torch
import torchvision.transforms as T
from PIL import Image
import cv2

def approximate_matrix_by_rank(matrix, rank=1):
    """
    Approximates a given matrix by another matrix with a specified rank,
    minimizing the Frobenius norm of the difference between the two matrices.

SqrtRyan

from rp import *
os.chdir("/efs/users/ryan.burgert/public/GoWithTheFlow/InferenceForPaper/batch_experiments/DAVIS_YuanchengPromptsMotionTransfer/infer_outputs/outputs/DAVIS_MOTION_TRANSFER_BATTERY_JAN29")

# FOR VIPSEG
davis_folder = "/efs/users/ryan.burgert/public/datasets/VIPSeg/videos/720p_49frames"
name_to_prompt = load_json("/efs/users/ryan.burgert/public/GoWithTheFlow/InferenceForPaper/batch_experiments/DAVIS_YuanchengPromptsMotionTransfer/target_prompt_VIPSeg_similar.json")
out_dir = "VIPSeg_out_jsons"


def get_name(x):

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import rp.git.CommonSource.raft as raft

video_urls = path_join(
    # "/nfs/ws3/hdd2/ws1nfs/ryan/CleanCode/Projects/Eyeline2024/gowiththeflowpaper.github.io.git/METRICS/flow_metrics",
    "https://go-with-the-flow-cvpr25.s3.us-east-1.amazonaws.com/Synthetics/",
    [
        "hhq_synthetics_010.mp4", #The racecar
        "hhq_synthetics_002.mp4", #The dog
        "hhq_synthetics_039.mp4", #The tornado
#NOW, DO ALL OF THEM...

SqrtRyan

import rp.git.CommonSource.raft as raft

video_urls = path_join(
    # "/nfs/ws3/hdd2/ws1nfs/ryan/CleanCode/Projects/Eyeline2024/gowiththeflowpaper.github.io.git/METRICS/flow_metrics",
    "https://go-with-the-flow-cvpr25.s3.us-east-1.amazonaws.com/Synthetics/",
    [
        "hhq_synthetics_010.mp4", #The racecar
        "hhq_synthetics_002.mp4", #The dog
        "hhq_synthetics_039.mp4", #The tornado
#NOW, DO ALL OF THEM...