meghbhalerao

from minigrid.wrappers import RGBImgPartialObsWrapper, ImgObsWrapper
from stable_baselines3.common.monitor import Monitor
from gymnasium.utils.play import play
from utils.utils import move_to_pos, get_pos_from_int, turn_and_explore # get_pos_from_int, turn_and_explore
from minigrid.core.actions import Actions
second_task = gym.make("MiniGrid-BlockedUnlockPickup-v0", render_mode = 'human')
# play(second_task,  
#     keys_to_action={
#         "w": np.int64(2),
#         "a": np.int64(0),

meghbhalerao

import gymnasium as gym
import minigrid
import numpy as np
import matplotlib.pyplot as plt
import sys
sys.path.append("/home/mb230/rice_coursework/f24/comp552/comp-552-assignment-backup/assignment5")
import wandb
run = wandb.init(
    project="comp552-a5", monitor_gym = True, sync_tensorboard=True
)

meghbhalerao

        sim_mat_dims = (len(dl.dataset), len(dl.dataset))
        print("Dimensions of similarity matrix is", sim_mat_dims)
        print("Making empty matrix to store similarities ......")
        feat_mat = np.empty(sim_mat_dims, dtype=np.float32)
        loss_fn = nn.CrossEntropyLoss(reduction='mean').to(self.device)
        for idx, data in tqdm(enumerate(dl)):
            loss_val = loss_fn(net(data[0].to(self.device)), data[1].to(self.device))
            grad_list = torch.autograd.grad(loss_val, inputs = [p for p in net.parameters() if p.requires_grad])
            feats_outer = [t.flatten() for t in grad_list]
            feats_outer = torch.cat(feats_outer)

meghbhalerao

[16:55:34] ----- Submarine logging started -----
[16:55:34] Started new execution run, signed 32-bit element types, signed 64-bit matrix indices ....
[16:55:34] Types: set elements signed 32-bit, set sizes signed 32-bit, set indices signed 32-bit, set iters unsigned 32-bit, matrix indices signed 64-bit.
[16:55:34] Using 22 threads for general operations.
[16:55:34] Command line: smraiz -flfilename /mnt/disks/spinning_scratch0/smrai-container-documentation/src/saved_results/tinyimagenet/tinyimagenet_convnetd4_1_features_ffcv_False_simeuclid_sim_or_dist.npy -sumsize 1 -cloglevel trace -floglevel trace -loglevel trace -clogtimestamps T -nochecks
[16:55:34] Loading FL matrix.
[16:55:34] RNPHR: Reading numpy file v1.0 with header size 70.
[16:55:34] RNPHR: Finished header of v1.0 numpy file, a 100000x100000 32-bit float (f4) matrix, element byte length 4, fortran false, endian little, endian N/A false.
[16:55:34] RMNF: allocating and reading 100000x100000 numpy matrix, skip_type_cast=true, fortran_order = false, n

meghbhalerao

import numpy as np
import time
import torch
import torch.nn as nn
import torch.nn.functional as F
from torchvision import datasets, transforms
import torchvision
from torch.utils.data import Dataset
from scipy.ndimage.interpolation import rotate as scipyrotate
import sys

meghbhalerao

from utils.evaluator_utils import EvaluatorUtils

ds_train = datasets.CIFAR10('data', train=True, download=True, transform=transform)
ds_test = datasets.CIFAR10('data', train=False, download=True, transform=transform)


images_all = [torch.unsqueeze(ds_train[i][0], dim=0) for i in range(len(ds_train))]
labels_all = [ds_train[i][1] for i in range(len(ds_train))]
class_pos_list = []

meghbhalerao

import numpy as np
import time
import torch
import torch.nn as nn
import torch.nn.functional as F
from torchvision import datasets, transforms
import torchvision
from torch.utils.data import Dataset
from scipy.ndimage.interpolation import rotate as scipyrotate
import sys

meghbhalerao

import time
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
import os
import kornia as K
import tqdm
from torch.utils.data import Dataset
from torchvision import datasets, transforms

meghbhalerao

from turtle import forward
import torch.nn as nn
import torch.nn.functional as F
import torch
from typing import Dict, Iterable, Callable
import torchvision
from models.pretrained_implementations import resnet18_pret
from models.conv_iResNet import conv_iResNet
# Acknowledgement to
# https://github.com/kuangliu/pytorch-cifar,

meghbhalerao

DATA_DIR='./data/datasets'

MAX_DEPTH=15
MAX_NODES=30
SEARCH_METHOD=bfs
MODEL=LSTM
NUM_EPOCHS_MENTION_ONLY=1
NUM_EPOCHS_WITH_COHERENCE=30
BATCH_SIZE=32