ARC_DT/visualize_trace.py at main · GIST-DSLab/ARC_DT · GitHub

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import numpy as np
import pandas as pd
from functools import reduce
import operator

import torch
import os
import json
from pathlib import Path

import matplotlib.pyplot as plt
from matplotlib import colors


ACTION = [
    "clockwise_rotate",
    "counterclockwise_rotate",
    "horizontal_flip",
    "vertical_flip",
    "edit",
    "select",
    "move_left",
    "move_right",
    "move_up",
    "move_down",
    "none"
]

# 0~9까지는 색깔
# 10~19까지는 select된 색깔
def plot_one(ax, i):
    cmap = colors.ListedColormap(
        [
            '#000000', # 0 검은색
            '#0074D9', # 1 파란색
            '#FF4136', # 2 빨간색
            '#2ECC40', # 3 초록색
            '#FFDC00', # 4 노란색
            '#AAAAAA', # 5 회색
            '#F012BE', # 6 핑크색
            '#FF851B', # 7 주황색
            '#7FDBFF', # 8 하늘색
            '#870C25', # 9 적갈색
        ])
    #norm = colors.Normalize(vmin=0, vmax=9)
    norm = colors.Normalize(vmin=0, vmax=19)

    input_matrix = task["step"][i]
    ax.imshow(input_matrix, cmap=cmap, norm=norm)
    ax.grid(True,which='both',color='lightgrey', linewidth=0.5)
    ax.set_yticks([x-0.5 for x in range(1+len(input_matrix))])
    ax.set_xticks([x-0.5 for x in range(1+len(input_matrix[0]))])
    ax.set_xticklabels([])
    ax.set_yticklabels([])

    if(i==0):
        ax.set_title('input')
    elif(i==len(task["step"])-1):
        ax.set_title('output' + ' (' + task["step_function"][i-1]+')')
    else:
        ax.set_title('step: '+ str(i)+' ('+task["step_function"][i-1]+')')


def plot_task(task):
    """
    Plots the first train and test pairs of a specified task,
    using same color scheme as the ARC app
    """

    num_step = len(task["step"])
    fig, axs = plt.subplots(1, (num_step), figsize=(3*(num_step), 3*2))

    for i in range(num_step):
        plot_one(axs[i], i)


    plt.tight_layout()
    plt.show()

# Select json file
data_path = Path('/home/jovyan/workspace/Jaehyun/Mini_ARC_DT/')
training_path = data_path / 'mini_arc_train'
training_tasks = sorted(os.listdir(training_path))

# Show training set
for i in range(len(training_tasks)):
    task_file = str(training_path / training_tasks[i])

    with open(task_file, 'r') as f:
        task = json.load(f)

    #print("#{0}: {1}".format(i, training_tasks[i]))
    #plot_task(task)
    #print("1st grid: {0}".format(task["action_sequence"]["action_sequence"][:]["grid"]))
    #print("1st action: {0}".format(task["action_sequence"]["action_sequence"][0]))

    step_gap = 4
    total_step = len(task["action_sequence"]["action_sequence"])
    start_idx = np.random.randint(0, total_step-step_gap+1)
    actions_type = {
        "start":0,
        "undo":1,
        "edit":2,
        "copyFromInput":3,
        "rotate":4,
        "reflectY":5,
        "reflectX":6,
        "translate":7,
        "resizeOutputGrid":8,
        "resetOutputGrid":9,
        "selected_cells":10
    }
    actions_num = len(actions_type)
    states = []
    actions_str = []
    actions = []
    rtgs = []
    timesteps = []
    for i in range(start_idx, start_idx+step_gap):
        states.append(reduce(operator.add, task["action_sequence"]["action_sequence"][i]["grid"]))
        actions_str.append(task["action_sequence"]["action_sequence"][i]["action"]["tool"])
        actions.append(actions_type[task["action_sequence"]["action_sequence"][i]["action"]["tool"]])
        rtgs.append(float(i)/step_gap)
        timesteps.append(i)

    states = torch.IntTensor(states)
    actions = torch.IntTensor(actions)
    rtgs = torch.FloatTensor(rtgs)
    timesteps = torch.IntTensor(timesteps)

    print(states)
    print(actions)
    print(actions_str)
    print(rtgs)
    print(timesteps)
    #print(states.shape)