From: François Fleuret Date: Wed, 15 Feb 2023 19:50:16 +0000 (+0100) Subject: Added the copyright comment + reformatted with black. X-Git-Url: https://fleuret.org/cgi-bin/gitweb/gitweb.cgi?a=commitdiff_plain;ds=inline;p=path.git Added the copyright comment + reformatted with black. --- diff --git a/path.py b/path.py index c26afed..866eeb9 100755 --- a/path.py +++ b/path.py @@ -1,5 +1,10 @@ #!/usr/bin/env python +# Any copyright is dedicated to the Public Domain. +# https://creativecommons.org/publicdomain/zero/1.0/ + +# Written by Francois Fleuret + import sys, math, time, argparse import torch, torchvision @@ -10,43 +15,33 @@ from torch.nn import functional as F ###################################################################### parser = argparse.ArgumentParser( - description='Path-planning as denoising.', - formatter_class = argparse.ArgumentDefaultsHelpFormatter + description="Path-planning as denoising.", + formatter_class=argparse.ArgumentDefaultsHelpFormatter, ) -parser.add_argument('--nb_epochs', - type = int, default = 25) +parser.add_argument("--nb_epochs", type=int, default=25) -parser.add_argument('--batch_size', - type = int, default = 100) +parser.add_argument("--batch_size", type=int, default=100) -parser.add_argument('--nb_residual_blocks', - type = int, default = 16) +parser.add_argument("--nb_residual_blocks", type=int, default=16) -parser.add_argument('--nb_channels', - type = int, default = 128) +parser.add_argument("--nb_channels", type=int, default=128) -parser.add_argument('--kernel_size', - type = int, default = 3) +parser.add_argument("--kernel_size", type=int, default=3) -parser.add_argument('--nb_for_train', - type = int, default = 100000) +parser.add_argument("--nb_for_train", type=int, default=100000) -parser.add_argument('--nb_for_test', - type = int, default = 10000) +parser.add_argument("--nb_for_test", type=int, default=10000) -parser.add_argument('--world_height', - type = int, default = 23) +parser.add_argument("--world_height", type=int, default=23) -parser.add_argument('--world_width', - type = int, default = 31) +parser.add_argument("--world_width", type=int, default=31) -parser.add_argument('--world_nb_walls', - type = int, default = 15) +parser.add_argument("--world_nb_walls", type=int, default=15) -parser.add_argument('--seed', - type = int, default = 0, - help = 'Random seed (default 0, < 0 is no seeding)') +parser.add_argument( + "--seed", type=int, default=0, help="Random seed (default 0, < 0 is no seeding)" +) ###################################################################### @@ -57,24 +52,27 @@ if args.seed >= 0: ###################################################################### -label='' +label = "" -log_file = open(f'path_{label}train.log', 'w') +log_file = open(f"path_{label}train.log", "w") ###################################################################### + def log_string(s): - t = time.strftime('%Y%m%d-%H:%M:%S', time.localtime()) - s = t + ' - ' + s + t = time.strftime("%Y%m%d-%H:%M:%S", time.localtime()) + s = t + " - " + s if log_file is not None: - log_file.write(s + '\n') + log_file.write(s + "\n") log_file.flush() print(s) sys.stdout.flush() + ###################################################################### + class ETA: def __init__(self, n): self.n = n @@ -84,60 +82,74 @@ class ETA: if k > 0: t = time.time() u = self.t0 + ((t - self.t0) * self.n) // k - return time.strftime('%Y%m%d-%H:%M:%S', time.localtime(u)) + return time.strftime("%Y%m%d-%H:%M:%S", time.localtime(u)) else: return "n.a." + ###################################################################### device = torch.device("cuda" if torch.cuda.is_available() else "cpu") -log_string(f'device {device}') +log_string(f"device {device}") ###################################################################### -def create_maze(h = 11, w = 15, nb_walls = 10): + +def create_maze(h=11, w=15, nb_walls=10): a, k = 0, 0 while k < nb_walls: while True: if a == 0: - m = torch.zeros(h, w, dtype = torch.int64) - m[ 0, :] = 1 - m[-1, :] = 1 - m[ :, 0] = 1 - m[ :, -1] = 1 + m = torch.zeros(h, w, dtype=torch.int64) + m[0, :] = 1 + m[-1, :] = 1 + m[:, 0] = 1 + m[:, -1] = 1 r = torch.rand(4) if r[0] <= 0.5: - i1, i2, j = int((r[1] * h).item()), int((r[2] * h).item()), int((r[3] * w).item()) - i1, i2, j = i1 - i1%2, i2 - i2%2, j - j%2 + i1, i2, j = ( + int((r[1] * h).item()), + int((r[2] * h).item()), + int((r[3] * w).item()), + ) + i1, i2, j = i1 - i1 % 2, i2 - i2 % 2, j - j % 2 i1, i2 = min(i1, i2), max(i1, i2) - if i2 - i1 > 1 and i2 - i1 <= h/2 and m[i1:i2+1, j].sum() <= 1: - m[i1:i2+1, j] = 1 + if i2 - i1 > 1 and i2 - i1 <= h / 2 and m[i1 : i2 + 1, j].sum() <= 1: + m[i1 : i2 + 1, j] = 1 break else: - i, j1, j2 = int((r[1] * h).item()), int((r[2] * w).item()), int((r[3] * w).item()) - i, j1, j2 = i - i%2, j1 - j1%2, j2 - j2%2 + i, j1, j2 = ( + int((r[1] * h).item()), + int((r[2] * w).item()), + int((r[3] * w).item()), + ) + i, j1, j2 = i - i % 2, j1 - j1 % 2, j2 - j2 % 2 j1, j2 = min(j1, j2), max(j1, j2) - if j2 - j1 > 1 and j2 - j1 <= w/2 and m[i, j1:j2+1].sum() <= 1: - m[i, j1:j2+1] = 1 + if j2 - j1 > 1 and j2 - j1 <= w / 2 and m[i, j1 : j2 + 1].sum() <= 1: + m[i, j1 : j2 + 1] = 1 break a += 1 - if a > 10 * nb_walls: a, k = 0, 0 + if a > 10 * nb_walls: + a, k = 0, 0 k += 1 return m + ###################################################################### + def random_free_position(walls): p = torch.randperm(walls.numel()) k = p[walls.view(-1)[p] == 0][0].item() - return k//walls.size(1), k%walls.size(1) + return k // walls.size(1), k % walls.size(1) + def create_transitions(walls, nb): trans = walls.new_zeros((9,) + walls.size()) @@ -145,24 +157,30 @@ def create_transitions(walls, nb): i, j = random_free_position(walls) for k in range(t.size(0)): - di, dj = [ (0, 1), (1, 0), (0, -1), (-1, 0) ][t[k]] + di, dj = [(0, 1), (1, 0), (0, -1), (-1, 0)][t[k]] ip, jp = i + di, j + dj - if ip < 0 or ip >= walls.size(0) or \ - jp < 0 or jp >= walls.size(1) or \ - walls[ip, jp] > 0: + if ( + ip < 0 + or ip >= walls.size(0) + or jp < 0 + or jp >= walls.size(1) + or walls[ip, jp] > 0 + ): trans[t[k] + 4, i, j] += 1 else: trans[t[k], i, j] += 1 i, j = ip, jp - n = trans[0:8].sum(dim = 0, keepdim = True) + n = trans[0:8].sum(dim=0, keepdim=True) trans[8:9] = n trans[0:8] = trans[0:8] / (n + (n == 0).long()) return trans + ###################################################################### + def compute_distance(walls, i, j): max_length = walls.numel() dist = torch.full_like(walls, max_length) @@ -172,41 +190,50 @@ def compute_distance(walls, i, j): while True: pred_dist.copy_(dist) - d = torch.cat( - ( - dist[None, 1:-1, 0:-2], - dist[None, 2:, 1:-1], - dist[None, 1:-1, 2:], - dist[None, 0:-2, 1:-1] - ), - 0).min(dim = 0)[0] + 1 + d = ( + torch.cat( + ( + dist[None, 1:-1, 0:-2], + dist[None, 2:, 1:-1], + dist[None, 1:-1, 2:], + dist[None, 0:-2, 1:-1], + ), + 0, + ).min(dim=0)[0] + + 1 + ) dist[1:-1, 1:-1] = torch.min(dist[1:-1, 1:-1], d) dist = walls * max_length + (1 - walls) * dist - if dist.equal(pred_dist): return dist * (1 - walls) + if dist.equal(pred_dist): + return dist * (1 - walls) + ###################################################################### + def compute_policy(walls, i, j): distance = compute_distance(walls, i, j) distance = distance + walls.numel() * walls value = distance.new_full((4,) + distance.size(), walls.numel()) - value[0, : , 1: ] = distance[ : , :-1] - value[1, : , :-1] = distance[ : , 1: ] - value[2, 1: , : ] = distance[ :-1, : ] - value[3, :-1, : ] = distance[1: , : ] + value[0, :, 1:] = distance[:, :-1] + value[1, :, :-1] = distance[:, 1:] + value[2, 1:, :] = distance[:-1, :] + value[3, :-1, :] = distance[1:, :] - proba = (value.min(dim = 0)[0][None] == value).float() - proba = proba / proba.sum(dim = 0)[None] + proba = (value.min(dim=0)[0][None] == value).float() + proba = proba / proba.sum(dim=0)[None] proba = proba * (1 - walls) return proba + ###################################################################### -def create_maze_data(nb, h = 11, w = 17, nb_walls = 8, traj_length = 50): + +def create_maze_data(nb, h=11, w=17, nb_walls=8, traj_length=50): input = torch.empty(nb, 10, h, w) targets = torch.empty(nb, 2, h, w) @@ -218,8 +245,8 @@ def create_maze_data(nb, h = 11, w = 17, nb_walls = 8, traj_length = 50): eta = ETA(nb) for n in range(nb): - if n%(max(10, nb//1000)) == 0: - log_string(f'{(100 * n)/nb:.02f}% ETA {eta.eta(n+1)}') + if n % (max(10, nb // 1000)) == 0: + log_string(f"{(100 * n)/nb:.02f}% ETA {eta.eta(n+1)}") walls = create_maze(h, w, nb_walls) trans = create_transitions(walls, l[n]) @@ -234,18 +261,21 @@ def create_maze_data(nb, h = 11, w = 17, nb_walls = 8, traj_length = 50): return input, targets + ###################################################################### -def save_image(name, input, targets, output = None): + +def save_image(name, input, targets, output=None): input, targets = input.cpu(), targets.cpu() weight = torch.tensor( [ - [ 1.0, 0.0, 0.0 ], - [ 1.0, 1.0, 0.0 ], - [ 0.0, 1.0, 0.0 ], - [ 0.0, 0.0, 1.0 ], - ] ).t()[:, :, None, None] + [1.0, 0.0, 0.0], + [1.0, 1.0, 0.0], + [0.0, 1.0, 0.0], + [0.0, 0.0, 1.0], + ] + ).t()[:, :, None, None] # img_trans = F.conv2d(input[:, 0:5], weight) # img_trans = img_trans / img_trans.max() @@ -271,7 +301,7 @@ def save_image(name, input, targets, output = None): img_walls[:, None], # img_pi[:, None], img_dist[:, None], - ) + ) if output is not None: output = output.cpu() @@ -299,24 +329,22 @@ def save_image(name, input, targets, output = None): img_all.size(4), ) - torchvision.utils.save_image( - img_all, - name, - padding = 1, pad_value = 0.5, nrow = len(img) - ) + torchvision.utils.save_image(img_all, name, padding=1, pad_value=0.5, nrow=len(img)) + + log_string(f"Wrote {name}") - log_string(f'Wrote {name}') ###################################################################### + class Net(nn.Module): def __init__(self): super().__init__() nh = 128 - self.conv1 = nn.Conv2d( 6, nh, kernel_size = 5, padding = 2) - self.conv2 = nn.Conv2d(nh, nh, kernel_size = 5, padding = 2) - self.conv3 = nn.Conv2d(nh, nh, kernel_size = 5, padding = 2) - self.conv4 = nn.Conv2d(nh, 2, kernel_size = 5, padding = 2) + self.conv1 = nn.Conv2d(6, nh, kernel_size=5, padding=2) + self.conv2 = nn.Conv2d(nh, nh, kernel_size=5, padding=2) + self.conv3 = nn.Conv2d(nh, nh, kernel_size=5, padding=2) + self.conv4 = nn.Conv2d(nh, 2, kernel_size=5, padding=2) def forward(self, x): x = F.relu(self.conv1(x)) @@ -325,21 +353,29 @@ class Net(nn.Module): x = self.conv4(x) return x + ###################################################################### + class ResNetBlock(nn.Module): def __init__(self, nb_channels, kernel_size): super().__init__() - self.conv1 = nn.Conv2d(nb_channels, nb_channels, - kernel_size = kernel_size, - padding = (kernel_size - 1) // 2) + self.conv1 = nn.Conv2d( + nb_channels, + nb_channels, + kernel_size=kernel_size, + padding=(kernel_size - 1) // 2, + ) self.bn1 = nn.BatchNorm2d(nb_channels) - self.conv2 = nn.Conv2d(nb_channels, nb_channels, - kernel_size = kernel_size, - padding = (kernel_size - 1) // 2) + self.conv2 = nn.Conv2d( + nb_channels, + nb_channels, + kernel_size=kernel_size, + padding=(kernel_size - 1) // 2, + ) self.bn2 = nn.BatchNorm2d(nb_channels) @@ -348,19 +384,22 @@ class ResNetBlock(nn.Module): y = F.relu(x + self.bn2(self.conv2(y))) return y -class ResNet(nn.Module): - def __init__(self, - in_channels, out_channels, - nb_residual_blocks, nb_channels, kernel_size): +class ResNet(nn.Module): + def __init__( + self, in_channels, out_channels, nb_residual_blocks, nb_channels, kernel_size + ): super().__init__() self.pre_process = nn.Sequential( - nn.Conv2d(in_channels, nb_channels, - kernel_size = kernel_size, - padding = (kernel_size - 1) // 2), + nn.Conv2d( + in_channels, + nb_channels, + kernel_size=kernel_size, + padding=(kernel_size - 1) // 2, + ), nn.BatchNorm2d(nb_channels), - nn.ReLU(inplace = True), + nn.ReLU(inplace=True), ) blocks = [] @@ -369,7 +408,7 @@ class ResNet(nn.Module): self.resnet_blocks = nn.Sequential(*blocks) - self.post_process = nn.Conv2d(nb_channels, out_channels, kernel_size = 1) + self.post_process = nn.Conv2d(nb_channels, out_channels, kernel_size=1) def forward(self, x): x = self.pre_process(x) @@ -377,49 +416,53 @@ class ResNet(nn.Module): x = self.post_process(x) return x + ###################################################################### -data_filename = 'path.dat' +data_filename = "path.dat" try: input, targets = torch.load(data_filename) - log_string('Data loaded.') - assert input.size(0) == args.nb_for_train + args.nb_for_test and \ - input.size(1) == 10 and \ - input.size(2) == args.world_height and \ - input.size(3) == args.world_width and \ - \ - targets.size(0) == args.nb_for_train + args.nb_for_test and \ - targets.size(1) == 2 and \ - targets.size(2) == args.world_height and \ - targets.size(3) == args.world_width + log_string("Data loaded.") + assert ( + input.size(0) == args.nb_for_train + args.nb_for_test + and input.size(1) == 10 + and input.size(2) == args.world_height + and input.size(3) == args.world_width + and targets.size(0) == args.nb_for_train + args.nb_for_test + and targets.size(1) == 2 + and targets.size(2) == args.world_height + and targets.size(3) == args.world_width + ) except FileNotFoundError: - log_string('Generating data.') + log_string("Generating data.") input, targets = create_maze_data( - nb = args.nb_for_train + args.nb_for_test, - h = args.world_height, w = args.world_width, - nb_walls = args.world_nb_walls, - traj_length = (100, 10000) + nb=args.nb_for_train + args.nb_for_test, + h=args.world_height, + w=args.world_width, + nb_walls=args.world_nb_walls, + traj_length=(100, 10000), ) torch.save((input, targets), data_filename) except: - log_string('Error when loading data.') + log_string("Error when loading data.") exit(1) ###################################################################### for n in vars(args): - log_string(f'args.{n} {getattr(args, n)}') + log_string(f"args.{n} {getattr(args, n)}") model = ResNet( - in_channels = 10, out_channels = 2, - nb_residual_blocks = args.nb_residual_blocks, - nb_channels = args.nb_channels, - kernel_size = args.kernel_size + in_channels=10, + out_channels=2, + nb_residual_blocks=args.nb_residual_blocks, + nb_channels=args.nb_channels, + kernel_size=args.kernel_size, ) criterion = nn.MSELoss() @@ -429,8 +472,8 @@ criterion.to(device) input, targets = input.to(device), targets.to(device) -train_input, train_targets = input[:args.nb_for_train], targets[:args.nb_for_train] -test_input, test_targets = input[args.nb_for_train:], targets[args.nb_for_train:] +train_input, train_targets = input[: args.nb_for_train], targets[: args.nb_for_train] +test_input, test_targets = input[args.nb_for_train :], targets[args.nb_for_train :] mu, std = train_input.mean(), train_input.std() train_input.sub_(mu).div_(std) @@ -447,12 +490,13 @@ for e in range(args.nb_epochs): else: lr = 1e-3 - optimizer = torch.optim.Adam(model.parameters(), lr = lr) + optimizer = torch.optim.Adam(model.parameters(), lr=lr) acc_train_loss = 0.0 - for input, targets in zip(train_input.split(args.batch_size), - train_targets.split(args.batch_size)): + for input, targets in zip( + train_input.split(args.batch_size), train_targets.split(args.batch_size) + ): output = model(input) loss = criterion(output, targets) @@ -464,20 +508,31 @@ for e in range(args.nb_epochs): test_loss = 0.0 - for input, targets in zip(test_input.split(args.batch_size), - test_targets.split(args.batch_size)): + for input, targets in zip( + test_input.split(args.batch_size), test_targets.split(args.batch_size) + ): output = model(input) loss = criterion(output, targets) test_loss += loss.item() log_string( - f'{e} acc_train_loss {acc_train_loss / (args.nb_for_train / args.batch_size)} test_loss {test_loss / (args.nb_for_test / args.batch_size)} ETA {eta.eta(e+1)}' + f"{e} acc_train_loss {acc_train_loss / (args.nb_for_train / args.batch_size)} test_loss {test_loss / (args.nb_for_test / args.batch_size)} ETA {eta.eta(e+1)}" ) # save_image(f'train_{e:04d}.png', train_input[:8], train_targets[:8], model(train_input[:8])) # save_image(f'test_{e:04d}.png', test_input[:8], test_targets[:8], model(test_input[:8])) - save_image(f'train_{e:04d}.png', train_input[:8], train_targets[:8], model(train_input[:8])[:, 0:2]) - save_image(f'test_{e:04d}.png', test_input[:8], test_targets[:8], model(test_input[:8])[:, 0:2]) + save_image( + f"train_{e:04d}.png", + train_input[:8], + train_targets[:8], + model(train_input[:8])[:, 0:2], + ) + save_image( + f"test_{e:04d}.png", + test_input[:8], + test_targets[:8], + model(test_input[:8])[:, 0:2], + ) ######################################################################