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lightning_task.py

Source:lightning_task.py

1import torch2import numpy as np3from torch.utils.data import Dataset, DataLoader4import matplotlib.pyplot as plt5import pytorch_lightning as pl6import os7AVAIL_GPUS = min(1, torch.cuda.device_count())8BATCH_SIZE = 256 if AVAIL_GPUS else 649NUM_WORKERS = int(os.cpu_count() / 2)10class DMTS_Dataset(torch.utils.data.Dataset):11    'Characterizes a delay-match to sample task'12    def __init__(self, inps,out_des,list_IDs, labels,test_ons, distracted_bools):13        'Initialization'14        self.labels = labels15        self.list_IDs = list_IDs16        self.inps = inps17        self.out_des = out_des18        self.test_ons = test_ons19        self.distracted_bools = distracted_bools20    def __len__(self):21        'Denotes the total number of samples'22        return len(self.list_IDs)23    def __getitem__(self, index):24        'Generates one sample of data'25        # Select sample26        ID = self.list_IDs[index]27        # Load data and get label28        inp = self.inps[ID]29        y = self.labels[ID].long()30        out = self.out_des[ID]31        test_on = self.test_ons[ID]32        distracted_bool = self.distracted_bools[ID]33        34        return inp,out,y,test_on,distracted_bool35def generate_one_DMTS_IO(sample_mat, samp, noise_level, dt,alpha, time_limits = [-1,5.5], possible_delays = [1.  , 1.41, 2.  , 2.83, 4.  ], use_distractor = 1):36    37    num_samples = len(sample_mat)38    39    distractor1 = np.random.choice(np.delete(np.arange(num_samples),samp))40    41    #by convention the 8th and 9th samples are possible mid-delay distractors42    mid_delay_distractor_ind = np.random.choice([7,8])43    delay_length = 1000*np.random.choice(possible_delays)44    45    tvec = np.arange(time_limits[0],time_limits[1],1/1000)46    TIME_STEPS = len(tvec)47    48    49    inp = torch.zeros((TIME_STEPS,num_samples+1))   50    out_des = torch.zeros((TIME_STEPS,num_samples+1))     51    samp_on = np.argmin(np.abs(tvec-0))52    samp_off = samp_on + 50053    54    dis_on = int(delay_length/2) + samp_off55    dis_off  = dis_on + 25056    test_on = samp_off + int(delay_length)57    test_off = test_on + 500  58    59    #present sample60    inp[samp_on:samp_off,:-1] = sample_mat[samp]61    62    63    64    if np.heaviside(np.random.rand()-0.5,0):65        #present distractor on 50% of trials66        inp[dis_on:dis_off,:-1] = sample_mat[mid_delay_distractor_ind]67        distracted_bool = 168    else:69        distracted_bool = 070    71    #present test and sample72    inp[test_on:test_off,:-1] = sample_mat[samp] + sample_mat[distractor1]73    74    #fixate signal75    inp[0:test_on,-1] = 1 # fixation signal, answer when it goes off    76    77    #desired output78    #out_des[int(3000/dt):int(3500/dt),samp] = 179    out_des[test_on:,samp] = 180    out_des[0:test_on,-1] = 081    82    inp += np.sqrt(2/alpha)*noise_level*torch.randn(inp.shape)83    84    85    return inp[::dt] , out_des[::dt], int(test_on/dt), distracted_bool86def generate_DMTS(dt = 100, tau = 100, time_limits = [-1,5.5], num_samples = 8+2, variable_delay = True, mid_delay_distractor = True):87    88    """"89   Generates one delayed-match to sample dataset. 90    91    ARGS:92        -dt: timestep to use93        -time_limits: beginning and end of trial. sample On = 0s. Units of s. 94        -num_samples: size of sample pool95        96    97    98    RETURNS:99        -inps: inputs into the rnn, size batch by time by num_samples + 1100        -out_des: desired outputs from the rnn, size batch by time by num_samples + 1101        -partition: training and testing IDs102        -labels: sample label for each element in dataset103    """   104    #use binary encoding of sample images105    sample_mat = torch.eye( num_samples )106    107    TIME_STEPS = len(np.arange(time_limits[0],time_limits[1], dt/1000))108        109    noise_level = 0.01110    111    num_train = int(2**14)#int(0.6*0.5*(2**13))112    num_test = int(2**12)#int(0.4*0.5*(2**13))113    num_val = int(2**12)114    num_examples = num_train + num_test + num_val #int(0.5*(2**10))115    inps = torch.empty((num_examples,TIME_STEPS, num_samples+1), requires_grad = False)116    out_des = torch.empty((num_examples,TIME_STEPS, num_samples+1),requires_grad = False)117    test_ons = torch.empty((num_examples),requires_grad = False)118    distracted_bools = torch.empty((num_examples),requires_grad = False)119    120    labels = torch.empty(num_examples)121    list_IDs = []122    for i in range(num_examples):123        samp = np.random.choice(np.arange(num_samples-2))   124        inps[i],out_des[i],test_ons[i],distracted_bools[i] = generate_one_DMTS_IO(sample_mat = sample_mat, samp = samp, noise_level = noise_level, dt = dt, alpha = dt/tau,time_limits = time_limits)125        labels[i] = samp126        list_IDs.append(i)127    partition = {'train': list_IDs[:num_train], 'test': list_IDs[num_train: num_train+ num_test],'val':list_IDs[num_train + num_test: num_train+ num_test+ num_val]}128    129    return inps,out_des,partition,labels,test_ons,distracted_bools130def get_DMTS_training_test_val_sets(dt_ann,train_batch = 256,test_batch = 256,val_batch = 2**12):131    132    inps,out_des,partition,labels,test_ons,distracted_bools = generate_DMTS(dt = dt_ann)133    training_set = DMTS_Dataset(inps,out_des,partition['train'],labels,test_ons,distracted_bools)134    test_set = DMTS_Dataset(inps,out_des,partition['test'],labels,test_ons, distracted_bools)135    validation_set = DMTS_Dataset(inps,out_des,partition['val'],labels,test_ons, distracted_bools)136    137    138    return training_set,test_set,validation_set139class dDMTSDataModule(pl.LightningDataModule):140    def __init__(self, dt_ann: int = 15, batch_size: int = 256):141        super().__init__()142        self.dt_ann = dt_ann  143        self.batch_size = batch_size144            145    def setup(self,stage = None):146        self.training_data,self.test_data,self.validation_data = get_DMTS_training_test_val_sets(self.dt_ann)147    def train_dataloader(self):148        return DataLoader(self.training_data, batch_size=BATCH_SIZE, shuffle=True,num_workers = NUM_WORKERS,drop_last = True,pin_memory=False)149    def val_dataloader(self):150        return DataLoader(self.test_data, batch_size=2**11,num_workers = NUM_WORKERS, drop_last = True, pin_memory=False)151    def test_dataloader(self):...

main.py

Source:main.py

1def read_line(line):2    on = line.split()[0] == "on"  # True if on, False if off3    xyz = []4    for val in line.split()[1].split(","):5        xyz.append([int(i) for i in val.split("=")[1].split("..")])6    return on, xyz7def read_input(filename):8    ons = []9    xyzs = []10    with open(filename, "r") as fid:11        for line in fid.readlines():12            on, xyz = read_line(line.strip())13            ons.append(on)14            xyzs.append(xyz)15    return ons, xyzs16def on_cubes(ons, xyzs):17    output = set()18    for i in range(len(ons)):19        on = ons[i]20        xyz = xyzs[i]21        for x in range(max(xyz[0][0], -50), min(xyz[0][1], 50) + 1):22            for y in range(max(xyz[1][0], -50), min(xyz[1][1], 50) + 1):23                for z in range(max(xyz[2][0], -50), min(xyz[2][1], 50) + 1):24                    if on:25                        output.add((x, y, z))26                    else:27                        output.discard((x, y, z))28    return len(output)29def volume(xyz):30    return (31        (xyz[0][1] - xyz[0][0] + 1)32        * (xyz[1][1] - xyz[1][0] + 1)33        * (xyz[2][1] - xyz[2][0] + 1)34    )35def overlap(xyza, xyzb):36    xyzc = []37    for dim in range(3):38        if xyza[dim][0] <= xyzb[dim][0] <= xyza[dim][1]:39            xyzc.append([xyzb[dim][0], min(xyza[dim][1], xyzb[dim][1])])40        elif xyza[dim][0] <= xyzb[dim][1] <= xyza[dim][1]:41            xyzc.append([xyza[dim][0], min(xyza[dim][1], xyzb[dim][1])])42        elif xyzb[dim][0] < xyza[dim][0] and xyzb[dim][1] > xyza[dim][1]:43            xyzc.append(xyza[dim])44        else:45            return []46    return xyzc47def split(xyz, overlap):48    new_xyzs = []49    new_xlims = [xyz[0][0], xyz[0][1]]50    if xyz[0][0] < overlap[0][0]:51        new_xyzs.append([[xyz[0][0], overlap[0][0] - 1], xyz[1], xyz[2]])52        new_xlims[0] = overlap[0][0]53    if overlap[0][1] < xyz[0][1]:54        new_xyzs.append([[overlap[0][1] + 1, xyz[0][1]], xyz[1], xyz[2]])55        new_xlims[1] = overlap[0][1]56    new_ylims = [xyz[1][0], xyz[1][1]]57    if xyz[1][0] < overlap[1][0]:58        new_xyzs.append([new_xlims, [xyz[1][0], overlap[1][0] - 1], xyz[2]])59        new_ylims[0] = overlap[1][0]60    if overlap[1][1] < xyz[1][1]:61        new_xyzs.append([new_xlims, [overlap[1][1] + 1, xyz[1][1]], xyz[2]])62        new_ylims[1] = overlap[1][1]63    if xyz[2][0] < overlap[2][0]:64        new_xyzs.append([new_xlims, new_ylims, [xyz[2][0], overlap[2][0] - 1]])65    if overlap[2][1] < xyz[2][1]:66        new_xyzs.append([new_xlims, new_ylims, [overlap[2][1] + 1, xyz[2][1]]])67    return new_xyzs68def maybe_better_on_cubes(ons, xyzs):69    on_cubes = []70    for i in range(len(ons)):71        on = ons[i]72        xyz = xyzs[i]73        if on:74            on_cubes.append(xyz)75        else:76            add = []77            remove = []78            for cube in on_cubes:79                cube_overlap = overlap(xyz, cube)80                if cube_overlap:81                    remove.append(cube)82                    add += split(cube, cube_overlap)83            for cube in remove:84                on_cubes.remove(cube)85            on_cubes += add86    final_cubes = []87    while on_cubes:88        xyz = on_cubes[0]89        on_cubes = on_cubes[1:]90        for fc in final_cubes:91            cube_overlap = overlap(xyz, fc)92            if cube_overlap:93                on_cubes += split(xyz, fc)94                break95        else:96            final_cubes.append(xyz)97    return sum([volume(cube) for cube in final_cubes])98if __name__ == "__main__":99    test_ons, test_xyzs = read_input("test_input.txt")100    assert on_cubes(test_ons, test_xyzs) == 590784101    ons, xyzs = read_input("input.txt")102    print(on_cubes(ons, xyzs))103    assert maybe_better_on_cubes(test_ons, test_xyzs) == 590784104    test_ons, test_xyzs = read_input("test_input_2.txt")105    assert maybe_better_on_cubes(test_ons, test_xyzs) == 2758514936282235...

test_ons.py

Source:test_ons.py

1import tigercontrol2from tigercontrol.utils.optimizers.ons import ONS3from tigercontrol.utils.optimizers.losses import mse4import matplotlib.pyplot as plt5def test_ons(show=False):6    environment = tigercontrol.environment('LDS')7    x = environment.reset(p=2,q=0)8    controller = tigercontrol.controllers('LSTM')9    controller.initialize(n=1, m=1, l=5, h=10, optimizer=ONS) # initialize with class10    controller.predict(1.0) # call controllers to verify it works11    controller.update(1.0)12    optimizer = ONS(learning_rate=0.001)13    controller = tigercontrol.controllers('LSTM')14    controller.initialize(n=1, m=1, l=3, h=10, optimizer=optimizer) # reinitialize with instance15    loss = []16    for t in range(1000):17        y_pred = controller.predict(x)18        y_true = environment.step()19        loss.append(mse(y_pred, y_true))20        controller.update(y_true)21        x = y_true22    if show:23        plt.plot(loss)24        plt.show(block=False)25        plt.pause(3)26        plt.close()27    print("test_ons passed")28if __name__ == "__main__":...

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