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

        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

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

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),