From 3c5ae153538f958fab51d87c006a752416739927 Mon Sep 17 00:00:00 2001 From: Wesxdz Date: Tue, 1 Nov 2022 10:48:17 -0400 Subject: [PATCH] Use faiss scalar quantizer to reduce dimensionality --- config.py | 10 +-- generate_dataset.py | 18 +++-- pca.py | 11 +++ scqu.py | 34 +++++++++ train.py | 171 ++++++++++++++++++++++++++++++-------------- voxel.blend | Bin 12355802 -> 12382422 bytes voxel.blend1 | Bin 0 -> 12404042 bytes voxel_dataset.py | 15 ++-- 8 files changed, 186 insertions(+), 73 deletions(-) create mode 100644 pca.py create mode 100644 scqu.py create mode 100644 voxel.blend1 diff --git a/config.py b/config.py index e3090dc..7f7b107 100644 --- a/config.py +++ b/config.py @@ -1,8 +1,10 @@ +downsample = 4 # C x H x W -screen_size = (4, 360, 640) +screen_size = (4, int(360/downsample), int(640/downsample)) +# screen_size = (128, 128) # TODO: The screen size is too large to fit in GPU memory and is probably overkill # Let's downscale the resolution of the input images and represent the four channels as one -screen_input_size = 4 * 360 * 640 -voxel_grid_size = (64*64,) -dataset_size = 1 \ No newline at end of file +# screen_input_size = 4 * 360 * 640 +voxel_grid_size = (16*16,) +dataset_size = 32 \ No newline at end of file diff --git a/generate_dataset.py b/generate_dataset.py index 62f8bc2..d6b0037 100644 --- a/generate_dataset.py +++ b/generate_dataset.py @@ -26,7 +26,7 @@ def set_random_seed(self): pool_collection_name = "voxels" block_size = 0.0 -chunk_size = 16 +chunk_size = 8 # TODO: Will need to create a distinct placer for each voxel type, or encode objects in vertex data placer = bpy.data.objects["placer"] @@ -63,7 +63,7 @@ def spawn_occlusion_chunks(radius, noise, d): verts = [] rgb = [] colors = [] - for y in range(0, radius*2): + for y in range(0, radius): for x in range(0, radius*2): chunk_origin = Vector((x*chunk_size, y*chunk_size, 0.0)) spawn_voxel_occlusion_heightmap(noise, verts, colors, rgb, chunk_origin.x, chunk_origin.y, chunk_size, chunk_size, 1.0) @@ -73,7 +73,7 @@ def spawn_occlusion_chunks(radius, noise, d): bpy.context.object.modifiers['GeometryNodes'].node_group = bpy.data.node_groups['Geometry Nodes'] bpy.context.object.modifiers['GeometryNodes']['Input_2'] = bpy.data.objects['occlusion_voxel'] bpy.ops.geometry.color_attribute_add(name="color", color=(1.0, 0, 0.0, 1)) - player_floor = noise[chunk_size*radius][chunk_size*radius] + player_floor = noise[0][chunk_size*radius] ob.location.z = -player_floor*block_size ob.scale = Vector((block_size, block_size, block_size)) for i, cd in enumerate(bpy.context.active_object.data.attributes['color'].data): @@ -86,7 +86,7 @@ def spawn_occlusion_chunks(radius, noise, d): def spawn_chunks(radius, noise): terrain = [] verts = [] - for y in range(0, radius*2): + for y in range(0, radius): for x in range(0, radius*2): chunk_origin = Vector((x*chunk_size, y*chunk_size, 0.0)) spawn_voxel_heightmap(noise, verts, chunk_origin.x, chunk_origin.y, chunk_size, chunk_size, 1.0) @@ -108,16 +108,20 @@ def spawn_chunks(radius, noise): bpy.context.object.modifiers['GeometryNodes']['Input_2'] = bpy.data.objects[vt + '_voxel'] ob.scale = Vector((block_size, block_size, block_size)) - player_floor = noise[chunk_size*radius][chunk_size*radius] + player_floor = noise[0][chunk_size*radius] ob.location.z = -player_floor*block_size terrain.append(ob) bpy.data.objects["Camera"].location.x = chunk_size*radius*block_size - bpy.data.objects["Camera"].location.y = chunk_size*radius*block_size + bpy.data.objects["Camera"].location.y = 0 return terrain bpy.context.scene.render.image_settings.color_depth = "16" bpy.context.scene.render.image_settings.compression = 0 +downsample = 4 +bpy.context.scene.render.resolution_x = int(640/downsample) +bpy.context.scene.render.resolution_y = int(360/downsample) + dataset_size = 32 for d in range(dataset_size): # Recreate noise layers for random seeds! @@ -134,7 +138,7 @@ def spawn_chunks(radius, noise): bpy.data.objects["Camera"].rotation_euler.x = math.radians(random.randint(45, 115)) bpy.data.objects["Camera"].rotation_euler.z = math.radians(random.randint(-44, 44)) # Spawn random terrain - radius = 2 + radius = 1 noise = np.zeros(shape=[chunk_size*radius*2, chunk_size*radius*2]) for layer in noise_layers: opensimplex.seed(layer.seed) diff --git a/pca.py b/pca.py new file mode 100644 index 0000000..67c7ab3 --- /dev/null +++ b/pca.py @@ -0,0 +1,11 @@ +import faiss +import numpy as np + +# random training data +mt = np.random.rand(1, 4).astype('float32') +mat = faiss.PCAMatrix (4, 2) +mat.train(mt) +assert mat.is_trained +tr = mat.apply(mt) +# print this to show that the magnitude of tr's columns is decreasing +print(tr.shape) \ No newline at end of file diff --git a/scqu.py b/scqu.py new file mode 100644 index 0000000..4af1b9c --- /dev/null +++ b/scqu.py @@ -0,0 +1,34 @@ +import faiss +import numpy as np +from skimage import io, transform +import os +from einops import rearrange +import sys + +d = 4 # data dimension +dataset_size = 32 + +# train set +img = io.imread(os.path.join("data", "voxels_0.png")) +xt = rearrange(img, 'h w c -> (h w) c').astype('float32') +print(sys.getsizeof(xt)) + +# QT_8bit allocates 8 bits per dimension (QT_4bit also works) +sq = faiss.ScalarQuantizer(d, faiss.ScalarQuantizer.QT_4bit) +sq.train(xt) + +# encode +codes = sq.compute_codes(xt) +print(sys.getsizeof(codes)) +print(type(codes[0][1])) + +# decode +x2 = sq.decode(codes) + +comp = rearrange(x2, '(h w) c -> h w c', h=90, w=160) +io.imsave("test.png", comp) + +# compute reconstruction error +# avg_relative_error = ((xt - x2)**2).sum() / (xt ** 2).sum() +# print(codes) +# print(avg_relative_error) \ No newline at end of file diff --git a/train.py b/train.py index 70e67b8..7766677 100644 --- a/train.py +++ b/train.py @@ -6,17 +6,17 @@ from halonet_pytorch import HaloAttention from voxel_dataset import VoxelViewDataset from config import * -from math import prod -# from einops import +import numpy as np +from einops import rearrange, reduce, repeat # Hyperparameters num_epochs = 80 -batch_size = 100 +batch_size = 1 learning_rate = 0.001 training_dataset = VoxelViewDataset(12, "data") -dataloader = DataLoader(training_dataset, batch_size=4, - shuffle=True, num_workers=3) +dataloader = DataLoader(training_dataset, batch_size=1, + shuffle=True, num_workers=1) for i_batch, sample_batched in enumerate(dataloader): print(sample_batched.keys()) @@ -31,50 +31,117 @@ # based on ResNet architecture # https://arxiv.org/pdf/1512.03385.pdf -class NeuralNetwork(nn.Module): - def __init__(self) -> None: - super(NeuralNetwork, self).__init__() - view_ch = prod(screen_size) - voxel_ch = prod(voxel_grid_size) - self.residual = nn.Sequential( - nn.Conv2d(view_ch, view_ch, (7, 7, ), - nn.Conv2d(view_ch, view_ch, (7, 7)), - nn. - nn.AvgPool2d(), - nn.Linear(view_ch, voxel_ch) - ) - - -# class NeuralNetwork(nn.Module): -# def __init__(self): -# super(NeuralNetwork, self).__init__() -# self.flatten = nn.Flatten() -# self.linear_relu_stack = nn.Sequential( -# HaloAttention( -# dim = screen_input_size/4, -# block_size = 8, -# halo_size = 4, -# dim_head = 64, -# heads = 4 -# ), -# nn.ReLU(), -# nn.Linear(screen_input_size/4, voxel_grid_size[0]) -# ) - -# def forward(self, x): -# x = self.flatten(x) -# logits = self.linear_relu_stack(x) -# return logits - -# model = NeuralNetwork().to(device) -# print(model) - -# print(model) - - -# def get_reward(actual, prediction, rewards): -# reward = 0 -# for i in len(actual): -# if actual[i] == prediction[i]: -# reward += rewards[i] -# return reward \ No newline at end of file +# https://github.com/yunjey/pytorch-tutorial/blob/master/tutorials/02-intermediate/deep_residual_network/main.py + +def conv3x3(in_channels, out_channels, stride=1): + return nn.Conv2d(in_channels, out_channels, kernel_size=3, stride=stride, padding=1, bias=False) + +view_channels = np.prod(screen_size) +voxel_channels = np.prod(voxel_grid_size) + +# Residual block +class ResidualBlock(nn.Module): + def __init__(self, in_channels, out_channels, stride=1, downsample=None) -> None: + super(ResidualBlock, self).__init__() + # TODO Replace convolutional layers with HaloAttention + self.conv1 = conv3x3(in_channels, out_channels, stride) + self.bn1 = nn.BatchNorm2d(out_channels) + self.relu = nn.ReLU(inplace=True) + self.conv2 = conv3x3(out_channels, out_channels) + self.bn2 = nn.BatchNorm2d(out_channels) + self.downsample = downsample + + def forward(self, x): + residual = x + out = self.conv1(x) + out = self.bn1(out) + out = self.relu(out) + out = self.conv2d(out) + out = self.bn2(out) + if self.downsample: + residual = self.downsample(x) + out += residual + out = self.relu(out) + return out + +class ResNet(nn.Module): + def __init__(self, block, layers) -> None: + super(ResNet, self).__init__() + self.in_channels = view_channels + self.conv = conv3x3(3, view_channels) + self.bn = nn.BatchNorm2d(view_channels) + self.relu = nn.ReLU(inplace=True) + self.layer1 = self.make_layer(block, view_channels, layers[0]) + self.layer2 = self.make_layer(block, view_channels*2, layers[1], 2) + self.layer3 = self.make_layer(block, view_channels*4, layers[2], 2) + self.avg_pool = nn.AvgPool2d(view_channels/2) + self.fc = nn.Linear(view_channels*4, voxel_channels) + + def make_layer(self, block, out_channels, blocks, stride=1): + downsample = None + if stride != 1 or self.in_channels != out_channels: + downsample = nn.Sequential( + conv3x3(self.in_channels, out_channels, stride=stride), + nn.BatchNorm2d(out_channels) + ) + layers = [] + layers.append(block(self.in_channels, out_channels, stride, downsample)) + self.in_channels = out_channels + for i in range(1, blocks): + layers.append(block(out_channels, out_channels)) + return nn.Sequential(*layers) + + def forward(self, x): + out = self.conv(x) + out = self.bn(out) + out = self.relu(out) + out = self.layer1(out) + out = self.layer2(out) + out = self.layer3(out) + out = self.avg_pool(out) + out = out.view(out.size(0), -1) + out = self.fc(out) + return out + +def get_reward(actual, prediction, rewards): + reward = 0 + for i in len(actual): + if actual[i] == prediction[i]: + reward += rewards[i] + return reward + +model = ResNet(ResidualBlock, [2, 2, 2]).to(device) + +criterion = nn.CrossEntropyLoss() +optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate) + +def update_lr(optimizer, lr): + for param_group in optimizer.param_groups: + param_group['lr'] = lr + +total_step = len(training_dataset) +curr_lr = learning_rate +for epoch in range(num_epochs): + for i, sample in enumerate(training_dataset): + outputs = model() + loss = criterion(outputs, sample['grid']) + + optimizer.zero_grad() + loss.backward() + optimizer.step() + + if (i+1) % 100 == 0: + print ("Epoch [{}/{}], Step [{}/{}] Loss: {:.4f}" + .format(epoch+1, num_epochs, i+1, total_step, loss.item())) + + # Decay learning rate + if (epoch+1) % 20 == 0: + curr_lr /= 3 + update_lr(optimizer, curr_lr) + +# model.eval() +# with torch.no_grad(): + # for sample in test_dataset: + + +torch.save(model.state_dict(), 'voxelsight.ckpt') \ No newline at end of file diff --git a/voxel.blend b/voxel.blend index f887bee6d8e3dbe6fcfd21522ac890cff3ecfdfb..fdc4347246d431943b3f20ff7a618eda1da5b0bc 100644 GIT binary patch literal 12382422 zcmeEv4S*a)m49zG2@M8_h!6#iA&IccSNIMf*=%+{~*Y$qWJvG%|vy%@( zcQRXD-S546^{ZE}s;jH37A$FB*4Ex}?D~`DoYX<5SBil_HQLkRp&GkRp&GkRp&G zkRp&GkRp&GkRp&GkRp&GkRp&GkRp&GkRp&GkRp&GkRp&GkRp&GkRp&GkRp&GkRp&G zkRp&GkRp&GkRp&GkRp&GkRp&GkRp&GkRp&GkRp&GkRp&GkRp&GkRp&GkRp&GkRp&G zkRp&GkRp&GkRp&GkRp&GkRp&GkRp&GkRp&GkRp&GkRp&GkRp&GP&)$Ynwzy_mvW{E zqzI%4qzI%4qzI%4qzI%4qzI%4qzI%4qzI%4qzI%4qzI%4qzI%4qzI%4qzI%4qzLSK z1k!h&yS@xk4N?SB1X2W21X2W21X2W21X2W21X2W21X2W21a>b37A;x6V8vw1x?}OZ z?|$)0%eslwYoK2ylR0&&bxgz2qON0q@x?iG-<~%2&m20P=Po;e!tycQn#AY7zT(Z@ z9IiR>^b@;#^QUy3e$uHNPmdIV6oFSR0_QEYtj$x%f86vo*Kh3GsQgS^M9y1!USVKq 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zf*FSZbG;4kkzkrD_9ZRG&xX4l9Jo2b1^(a$5A>$q1mGVSJX((eLBV76;NVat1Y|u< zC}vo&tkqzhMzBkX7CctxoK*B2YxI30IDsb@`A?{ zk_AuDlS2x0OBp;7SrdsjF;cfS{2H|#LO}Y45ja`PH`4%E{UFI_OdN**k0&&)QA-vDF`?g?vBo)~LVM1FeY#v`ADdG}wV zL~`+K(`opHYCm8f9%;zElkt@)yu+s8HpNIcI zddqxLR<5%J%RJ&V>hUyWOhbl?y+Cc+`;_@=_kPq@h0Z78LSP*jp6=Bo$b{} U>Mm=A@PDKfD3L!e;r{jae (w h) c') - print(colors.shape) - pq = faiss.ScalarQuantizer(d, cs) - pq.train(colors) - # codes = pq.compute_codes(colors) - # print(codes) + xt = rearrange(img, 'h w c -> (h w) c').astype('float32') + sq = faiss.ScalarQuantizer(screen_size[0], faiss.ScalarQuantizer.QT_4bit) + sq.train(xt) + codes = sq.compute_codes(xt) return torch.from_numpy(img) def get_voxel_grid(self, idx):

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