DanielTakeshi

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DanielTakeshi

"""
Quick script I ran to visualuze some A2C results. Just run `python plot.py` in the same directory 
as where the stuff in `files` is located.
"""
import argparse, csv, os, pickle, sys, matplotlib
from os.path import join
matplotlib.use('Agg')
import matplotlib.pyplot as plt
plt.style.use('seaborn-darkgrid')
import numpy as np

DanielTakeshi

"""
For the BAIR Blog post.
(c) 2018 by Daniel Seita (and Michael Laskey).
"""
import numpy as np
import cv2 


def depth_to_3ch(img, cutoff):
    """Useful to turn the background into black into the depth images.

DanielTakeshi

import copy, cv2, os, sys, pickle, time
import numpy as np
from os.path import join

TARGET = 'tmp/'
RAW_PICKLE_FILE = 'data_raw_115_items.pkl'

def prepare_data():
    """Create the appropriate data for PyTorch using `ImageFolder`. From:

DanielTakeshi

import torch
import torch.nn as nn
import torch.optim as optim
from torch.optim import lr_scheduler
import torchvision.models as models
from torchvision import datasets, transforms
import copy, cv2, os, sys, pickle, time
import numpy as np
from os.path import join

DanielTakeshi

I am testing different PyTorch transforms on my data, and saving sample
images as they are loaded during training. See examples below.

DanielTakeshi

"""Some exception testing.

The code in the `try` block will stop as soon as any exception is encountered.

https://realpython.com/python-exceptions/
https://realpython.com/the-most-diabolical-python-antipattern/
"""

a = 5

DanielTakeshi

"""
How we might sample from a log-uniform distribution
https://stats.stackexchange.com/questions/155552/what-does-log-uniformly-distribution-mean

Only run one of these three cases at a time, otherwise the plots update each
other. Run with these versions:

matplotlib      3.2.1
numpy           1.18.3
"""

DanielTakeshi

import gym
import numpy as np
import imageio

# Make the environment and the initial observation `o`.
env_name = 'Walker2d-v3'
env = gym.make(env_name)
obs_dim = env.observation_space.shape
act_dim = env.action_space.shape[0]
act_limit = env.action_space.high[0]

DanielTakeshi

import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
plt.style.use('seaborn')
import numpy as np

nrows, ncols = 1, 2
fig, ax = plt.subplots(nrows, ncols, sharey=False, squeeze=True, figsize=(9*ncols, 6*nrows))
x = np.arange(100)
y0 = x + np.random.normal(loc=0.0, scale=10.0, size=100)