From 4ebaa1c1fc59290260df6f345d9e197efdfa937a Mon Sep 17 00:00:00 2001 From: FaezTurboGTS2000 Date: Thu, 11 Jul 2024 19:54:44 +0000 Subject: [PATCH 01/23] first commit --- modulus/models/topodiff/__init__.py | 19 ++ modulus/models/topodiff/diffusion.py | 65 ++++++ modulus/models/topodiff/topodiff.py | 335 +++++++++++++++++++++++++++ modulus/models/topodiff/train.py | 83 +++++++ modulus/models/topodiff/utils.py | 65 ++++++ 5 files changed, 567 insertions(+) create mode 100644 modulus/models/topodiff/__init__.py create mode 100644 modulus/models/topodiff/diffusion.py create mode 100644 modulus/models/topodiff/topodiff.py create mode 100644 modulus/models/topodiff/train.py create mode 100644 modulus/models/topodiff/utils.py diff --git a/modulus/models/topodiff/__init__.py b/modulus/models/topodiff/__init__.py new file mode 100644 index 0000000000..ae9f66a7f2 --- /dev/null +++ b/modulus/models/topodiff/__init__.py @@ -0,0 +1,19 @@ +# SPDX-FileCopyrightText: Copyright (c) 2023 - 2024 NVIDIA CORPORATION & AFFILIATES. +# SPDX-FileCopyrightText: All rights reserved. +# SPDX-License-Identifier: Apache-2.0 +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +from .topodiff import * +from .utils import * +from .diffusion import * diff --git a/modulus/models/topodiff/diffusion.py b/modulus/models/topodiff/diffusion.py new file mode 100644 index 0000000000..3c69e4a81c --- /dev/null +++ b/modulus/models/topodiff/diffusion.py @@ -0,0 +1,65 @@ +# SPDX-FileCopyrightText: Copyright (c) 2023 - 2024 NVIDIA CORPORATION & AFFILIATES. +# SPDX-FileCopyrightText: All rights reserved. +# SPDX-License-Identifier: Apache-2.0 +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import torch +import torch.nn as nn +import torch.nn.functional as F + +class Diffusion: + + def __init__(self, n_steps=1000, min_beta=10**-4, max_beta=0.02, device='cpu'): + + self.n_steps = n_steps + self.device = device + + self.betas = torch.linspace(min_beta, max_beta, self.n_steps).to(device) + + self.alphas = 1 - self.betas + + self.alpha_bars = torch.cumprod(self.alphas, 0).to(device) + + self.alpha_bars_prev = F.pad(self.alpha_bars[:-1], [1,0], 'constant', 0) + + self.posterior_variance = self.betas * (1. - self.alpha_bars_prev) / (1. - self.alpha_bars) + + self.loss = nn.MSELoss() + def q_sample(self, x0, t, noise=None): + + if noise is None: + noise = torch.rand_like(x0).to(self.device) + + alpha_bars = self.alpha_bars[t] + + x = alpha_bars.sqrt()[:,None, None, None] * x0 + (1 - alpha_bars).sqrt()[:, None, None, None] * noise + + return x + + def p_sample(self, model, xt, t, cons): + + return model(xt, cons, t) + + def train_loss(self, model, x0, cons): + + b, c, w, h = x0.shape + noise = torch.randn_like(x0).to(self.device) + + t = torch.randint(0, self.n_steps, (b,)).to(self.device) + + xt = self.q_sample(x0, t, noise) + + pred_noise = self.p_sample(model, xt, t, cons) + + return self.loss(pred_noise, noise) \ No newline at end of file diff --git a/modulus/models/topodiff/topodiff.py b/modulus/models/topodiff/topodiff.py new file mode 100644 index 0000000000..b37f2bd82e --- /dev/null +++ b/modulus/models/topodiff/topodiff.py @@ -0,0 +1,335 @@ +# SPDX-FileCopyrightText: Copyright (c) 2023 - 2024 NVIDIA CORPORATION & AFFILIATES. +# SPDX-FileCopyrightText: All rights reserved. +# SPDX-License-Identifier: Apache-2.0 +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +""" +Model architectures used in the paper Diffusion models beat gans on image synthesis". +""" + + +from dataclasses import dataclass +from typing import List + +import numpy as np +import torch +import torch.nn as nn +from torch.nn.functional import silu + +from ..diffusion import ( + Conv2d, + GroupNorm, + Linear, + PositionalEmbedding, + UNetBlock, +) +from ..meta import ModelMetaData +from ..module import Module + + +@dataclass +class MetaData(ModelMetaData): + name: str = "TopoDiff" + # Optimization + jit: bool = False + cuda_graphs: bool = False + amp_cpu: bool = False + amp_gpu: bool = True + torch_fx: bool = False + # Data type + bf16: bool = True + # Inference + onnx: bool = False + # Physics informed + func_torch: bool = False + auto_grad: bool = False + + +class TopoDiff(Module): + """ + Reimplementation of the ADM architecture, a U-Net variant, with optional + self-attention. + + This model supports conditional and unconditional setups, as well as several + options for various internal architectural choices such as encoder and decoder + type, embedding type, etc., making it flexible and adaptable to different tasks + and configurations. + + Parameters: + ----------- + img_resolution : int + The resolution of the input/output image. + in_channels : int + Number of channels in the input image. + out_channels : int + Number of channels in the output image. + label_dim : int, optional + Number of class labels; 0 indicates an unconditional model. By default 0. + augment_dim : int, optional + Dimensionality of augmentation labels; 0 means no augmentation. By default 0. + model_channels : int, optional + Base multiplier for the number of channels across the network, by default 192. + channel_mult : List[int], optional + Per-resolution multipliers for the number of channels. By default [1,2,3,4]. + channel_mult_emb : int, optional + Multiplier for the dimensionality of the embedding vector. By default 4. + num_blocks : int, optional + Number of residual blocks per resolution. By default 3. + attn_resolutions : List[int], optional + Resolutions at which self-attention layers are applied. By default [32, 16, 8]. + dropout : float, optional + Dropout probability applied to intermediate activations. By default 0.10. + label_dropout : float, optional + Dropout probability of class labels for classifier-free guidance. By default 0.0. + + Note: + ----- + Reference: Dhariwal, P. and Nichol, A., 2021. Diffusion models beat gans on image + synthesis. Advances in neural information processing systems, 34, pp.8780-8794. + + Note: + ----- + Equivalent to the original implementation by Dhariwal and Nichol, available at + https://github.com/openai/guided-diffusion + + Example: + -------- + >>> model = DhariwalUNet(img_resolution=16, in_channels=2, out_channels=2) + >>> noise_labels = torch.randn([1]) + >>> class_labels = torch.randint(0, 1, (1, 1)) + >>> input_image = torch.ones([1, 2, 16, 16]) + >>> output_image = model(input_image, noise_labels, class_labels) + """ + + def __init__( + self, + img_resolution: int, + in_channels: int, + out_channels: int, + label_dim: int = 0, + augment_dim: int = 0, + model_channels: int = 128, + channel_mult: List[int] = [1, 2, 3, 4], + channel_mult_emb: int = 4, + num_blocks: int = 3, + attn_resolutions: List[int] = [32, 16, 8], + dropout: float = 0.10, + label_dropout: float = 0.0, + ): + super().__init__(meta=MetaData()) + self.label_dropout = label_dropout + emb_channels = model_channels * channel_mult_emb + init = dict( + init_mode="kaiming_uniform", + init_weight=np.sqrt(1 / 3), + init_bias=np.sqrt(1 / 3), + ) + init_zero = dict(init_mode="kaiming_uniform", init_weight=0, init_bias=0) + block_kwargs = dict( + emb_channels=emb_channels, + channels_per_head=64, + dropout=dropout, + init=init, + init_zero=init_zero, + ) + + # Mapping. + self.map_noise = PositionalEmbedding(num_channels=model_channels) + self.map_augment = ( + Linear( + in_features=augment_dim, + out_features=model_channels, + bias=False, + **init_zero, + ) + if augment_dim + else None + ) + self.map_layer0 = Linear( + in_features=model_channels, out_features=emb_channels, **init + ) + self.map_layer1 = Linear( + in_features=emb_channels, out_features=emb_channels, **init + ) + self.map_label = ( + Linear( + in_features=label_dim, + out_features=emb_channels, + bias=False, + init_mode="kaiming_normal", + init_weight=np.sqrt(label_dim), + ) + if label_dim + else None + ) + + # Encoder. + self.enc = torch.nn.ModuleDict() + cout = in_channels + for level, mult in enumerate(channel_mult): + res = img_resolution >> level + if level == 0: + cin = cout + cout = model_channels * mult + self.enc[f"{res}x{res}_conv"] = Conv2d( + in_channels=cin, out_channels=cout, kernel=3, **init + ) + else: + self.enc[f"{res}x{res}_down"] = UNetBlock( + in_channels=cout, out_channels=cout, down=True, **block_kwargs + ) + for idx in range(num_blocks): + cin = cout + cout = model_channels * mult + self.enc[f"{res}x{res}_block{idx}"] = UNetBlock( + in_channels=cin, + out_channels=cout, + attention=(res in attn_resolutions), + **block_kwargs, + ) + skips = [block.out_channels for block in self.enc.values()] + + # Decoder. + self.dec = torch.nn.ModuleDict() + for level, mult in reversed(list(enumerate(channel_mult))): + res = img_resolution >> level + if level == len(channel_mult) - 1: + self.dec[f"{res}x{res}_in0"] = UNetBlock( + in_channels=cout, out_channels=cout, attention=True, **block_kwargs + ) + self.dec[f"{res}x{res}_in1"] = UNetBlock( + in_channels=cout, out_channels=cout, **block_kwargs + ) + else: + self.dec[f"{res}x{res}_up"] = UNetBlock( + in_channels=cout, out_channels=cout, up=True, **block_kwargs + ) + for idx in range(num_blocks + 1): + cin = cout + skips.pop() + cout = model_channels * mult + self.dec[f"{res}x{res}_block{idx}"] = UNetBlock( + in_channels=cin, + out_channels=cout, + attention=(res in attn_resolutions), + **block_kwargs, + ) + self.out_norm = GroupNorm(num_channels=cout) + self.out_conv = Conv2d( + in_channels=cout, out_channels=out_channels, kernel=3, **init_zero + ) + + def forward(self, x, cons, timesteps): + # Mapping. + emb = self.map_noise(timesteps) + + emb = silu(self.map_layer0(emb)) + emb = self.map_layer1(emb) + emb = silu(emb) + + x = torch.cat([x, cons], dim=1) + # Encoder. + skips = [] + for block in self.enc.values(): + x = block(x, emb) if isinstance(block, UNetBlock) else block(x) + skips.append(x) + + # Decoder. + for block in self.dec.values(): + if x.shape[1] != block.in_channels: + x = torch.cat([x, skips.pop()], dim=1) + x = block(x, emb) + x = self.out_conv(silu(self.out_norm(x))) + return x + +class UNetDecoder(Module): + + def __init__( + self, + in_channels: int, + out_channels: int, + model_channels: int = 128, + num_res_blocks: int = 4, + channel_mult: tuple = (1, 2, 4, 8), + channel_mult_emb: int = 4, + attention_resolutions: tuple = (2, 4, 8), + dropout=0, + output_prob=False): + + super().__init__() + + self.in_channels = in_channels + self.out_channels = out_channels + self.dropout = dropout + self.map_noise = PositionalEmbedding(num_channels = model_channels) + self.output_prob = output_prob + + ch = int(model_channels*channel_mult[0]) + self.conv = Conv2d(in_channels=in_channels, out_channels=ch,kernel=3) + + emb_channels = model_channels * channel_mult_emb + self.time_embed = nn.Sequential( + Linear(in_features=model_channels, out_features=emb_channels), + nn.SiLU(), + Linear(in_features=emb_channels, out_features=emb_channels) + ) + + ds = 1 + self.encoder = nn.ModuleList() + for level, mult in enumerate(channel_mult): + attention = ds in attention_resolutions + for i in range(num_res_blocks): + + down = (i == num_res_blocks - 1 and level != len(channel_mult) - 1) + + layer = UNetBlock(in_channels=ch, + out_channels=int(mult * model_channels), + emb_channels=emb_channels, + down=down, + attention=attention) + + self.encoder.append(layer) + ch = int(mult * model_channels) + ds *= 2 + + self.middle = nn.ModuleList([ + UNetBlock(in_channels=ch, out_channels=ch, emb_channels=emb_channels,attention=True), + UNetBlock(in_channels=ch, out_channels=ch, emb_channels=emb_channels,attention=False)]) + + self.out = nn.Sequential( + Linear(in_features=ch, out_features=2048), + GroupNorm(num_channels=2048), + nn.ReLU(), + nn.Dropout(p=dropout), + nn.Linear(in_features=2048, out_features=self.out_channels), + ) + + if self.output_prob: + self.out.append(nn.Sigmoid()) + + def forward(self, x, time_steps): + """ + param x: an [N x C x H x W] Tensor of inputs + param time_steps: a 1-D batch of timesteps + return: an [N x K] tensor of products + """ + emb = self.time_embed(self.map_noise(time_steps)) + + h = self.conv(x) + + for m in self.encoder: + h = m(h, emb) + + for m in self.middle: + h = m(h, emb) + return self.out(h.mean(dim=(2,3))) \ No newline at end of file diff --git a/modulus/models/topodiff/train.py b/modulus/models/topodiff/train.py new file mode 100644 index 0000000000..4524e9f5ce --- /dev/null +++ b/modulus/models/topodiff/train.py @@ -0,0 +1,83 @@ +# SPDX-FileCopyrightText: Copyright (c) 2023 - 2024 NVIDIA CORPORATION & AFFILIATES. +# SPDX-FileCopyrightText: All rights reserved. +# SPDX-License-Identifier: Apache-2.0 +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + + +import torch +import torch.nn as nn +from torch.optim import AdamW +import argparse +from tqdm import trange +import numpy as np + +from .topodiff import TopoDiff +from .diffusion import Diffusion +from .utils import DatasetTopoDiff, load_data_topodiff + +if __name__ == "__main__": + + + # Arguments + parser = argparse.ArgumentParser(description="Train the diffusion model") + parser.add_argument('--path_data', type=str, help='Path to the dataset folder') + parser.add_argument('--epochs', type=int, default=200000, help='Epoch number to train the model') + parser.add_argument('--batch_size', type=int, default=32, help='batch size to train the diffusion model') + + args = parser.parse_args() + + + print(args.path_data) + print(args.epochs) + + dataset_folder = args.path_data + + + topologies = np.load(dataset_folder + "topologies.npy").astype(np.float32) + constraints = np.load(dataset_folder + "constraints.npy", allow_pickle=True) + stress = np.load(dataset_folder + "vonmises.npy", allow_pickle=True) + strain = np.load(dataset_folder + "strain_energy.npy", allow_pickle=True) + load_imgs = np.load(dataset_folder + "load_ims.npy") + labels = np.load(dataset_folder + "Floating/training_labels.npy").astype(np.float32) + + device = torch.device('cuda:0') + model = TopoDiff(64, 6, 1, model_channels=128, attn_resolutions=[16,8]).to(device) + diffusion = Diffusion(n_steps=1000,device=device) + + batch_size = args.batch_size + data = load_data_topodiff( + topologies, constraints, stress, strain, load_imgs, batch_size= batch_size,deterministic=False + ) + + lr = 1e-4 + optimizer = AdamW(model.parameters(), lr=lr) + + prog = trange(args.epochs) + + for step in prog: + + tops, cons = next(data) + + tops = tops.float().to(device) + cons = cons.float().to(device) + + + losses = diffusion.train_loss(model, tops, cons) + + optimizer.zero_grad() + losses.backward() + optimizer.step() + + if step % 100 == 0: + print("epoch: %d, loss: %.5f" % (step, losses.item())) \ No newline at end of file diff --git a/modulus/models/topodiff/utils.py b/modulus/models/topodiff/utils.py new file mode 100644 index 0000000000..266ecfc3ca --- /dev/null +++ b/modulus/models/topodiff/utils.py @@ -0,0 +1,65 @@ +# SPDX-FileCopyrightText: Copyright (c) 2023 - 2024 NVIDIA CORPORATION & AFFILIATES. +# SPDX-FileCopyrightText: All rights reserved. +# SPDX-License-Identifier: Apache-2.0 +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import numpy as np +from torch.utils.data import Dataset, DataLoader + + +class DatasetTopoDiff(Dataset): + + def __init__(self, topologies, stress, strain, load_im, constraints): + + self.topologies = topologies + self.constraints = constraints + self.image_size = topologies.shape[1] + + self.stress = stress + self.strain = strain + self.load_im = load_im + + def __len__(self): + return self.topologies.shape[0] + + def __getitem__(self, idx): + + cons = self.constraints[idx] + + vol_frac = cons['VOL_FRAC'] + + cons = np.zeros((5, self.image_size, self.image_size)) + + cons[0] = self.stress[idx] + cons[1] = self.strain[idx] + cons[2] = self.load_im[idx][:,:,0] + cons[3] = self.load_im[idx][:,:,1] + cons[4] = np.ones((self.image_size,self.image_size)) * vol_frac + + return np.expand_dims(self.topologies[idx], 0) * 2 - 1, cons + +def load_data_topodiff(topologies, constraints, stress, strain, load_img, batch_size, deterministic=False): + dataset = DatasetTopoDiff(topologies, stress, strain, load_img, constraints) + + if deterministic: + loader = DataLoader( + dataset, batch_size=batch_size, shuffle=False, num_workers=1, drop_last=True + ) + else: + loader = DataLoader( + dataset, batch_size=batch_size, shuffle=True, num_workers=1, drop_last=True + ) + + while True: + yield from loader \ No newline at end of file From 989b97918d0e81e65ed44e37fe3a9540b75b7a7c Mon Sep 17 00:00:00 2001 From: FaezTurboGTS2000 Date: Sun, 28 Jul 2024 01:08:48 +0000 Subject: [PATCH 02/23] add README.md --- docs/img/topodiff.png | Bin 0 -> 617521 bytes examples/generative/topodiff/README.md | 24 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+ +

+ +## Dataset +Link to the dataset: [here](https://www.dropbox.com/home/decode_lab/Datasets/Public%20Documents/Topodiff_dataset) + + +## Citations +To cite this work, please use the following reference: + +```bibtex +@inproceedings{maze2023diffusion, + title={Diffusion models beat gans on topology optimization}, + author={Maz{\'e}, Fran{\c{c}}ois and Ahmed, Faez}, + booktitle={Proceedings of the AAAI conference on artificial intelligence}, + volume={37}, + number={8}, + pages={9108--9116}, + year={2023} +} +``` From 8c3444a9d240d5f1dc2b7f519360b7c4ba0e96b8 Mon Sep 17 00:00:00 2001 From: FaezTurboGTS2000 Date: Sun, 28 Jul 2024 01:12:15 +0000 Subject: [PATCH 03/23] add README.md --- docs/img/{ => topodiff_doc}/topodiff.png | Bin examples/generative/topodiff/README.md | 2 +- 2 files changed, 1 insertion(+), 1 deletion(-) rename docs/img/{ => topodiff_doc}/topodiff.png (100%) diff --git a/docs/img/topodiff.png b/docs/img/topodiff_doc/topodiff.png similarity index 100% rename from docs/img/topodiff.png rename to docs/img/topodiff_doc/topodiff.png diff --git a/examples/generative/topodiff/README.md b/examples/generative/topodiff/README.md index ea5747ab68..52499a8a3d 100644 --- a/examples/generative/topodiff/README.md +++ b/examples/generative/topodiff/README.md @@ -1,7 +1,7 @@ # TopoDiff

- +

## Dataset From 6834c52bbbeef840be42192903ddbb428fc14e51 Mon Sep 17 00:00:00 2001 From: FaezTurboGTS2000 Date: Sun, 28 Jul 2024 01:30:50 +0000 Subject: [PATCH 04/23] add README.md --- examples/generative/topodiff/README.md | 6 ++++++ 1 file changed, 6 insertions(+) diff --git a/examples/generative/topodiff/README.md b/examples/generative/topodiff/README.md index 52499a8a3d..d9ed69d8eb 100644 --- a/examples/generative/topodiff/README.md +++ b/examples/generative/topodiff/README.md @@ -1,4 +1,7 @@ # TopoDiff +We propose Topodiff, a conditional diffusion-model-based architecture to perform performance-aware and manufacturability-aware topology optimization that overcomes the issues of Generative Adversarial Networks (GANs) such as difficult to train, limited generalizability and neglecting manufacturability. Topodiff introduces a surrogate model-based guidance strategy that actively favors structures with low compliance and good manufacturability. +- Link to the paper: [Link](https://arxiv.org/abs/2208.09591) +- Link to the project: [Link](https://decode.mit.edu/projects/topodiff/)

@@ -22,3 +25,6 @@ To cite this work, please use the following reference: year={2023} } ``` + +## Contacts + From 1bed4b653c09c39470b808e3b845c540098d3a78 Mon Sep 17 00:00:00 2001 From: FaezTurboGTS2000 Date: Thu, 26 Sep 2024 17:40:13 +0000 Subject: [PATCH 05/23] revise for 2nd round code review --- examples/generative/topodiff/conf/config.yaml | 32 +++++ examples/generative/topodiff/train.py | 73 +++++++++++ .../generative/topodiff/train_classifier.py | 80 ++++++++++++ .../generative/topodiff/train_regressor.py | 107 ++++++++++++++++ examples/generative/topodiff/utils.py | 116 ++++++++++++++++++ modulus/models/topodiff/topodiff.py | 2 +- modulus/models/topodiff/train.py | 10 +- test/models/topodiff/test_topodiff.py | 57 +++++++++ 8 files changed, 475 insertions(+), 2 deletions(-) create mode 100644 examples/generative/topodiff/conf/config.yaml create mode 100644 examples/generative/topodiff/train.py create mode 100644 examples/generative/topodiff/train_classifier.py create mode 100644 examples/generative/topodiff/train_regressor.py create mode 100644 examples/generative/topodiff/utils.py create mode 100644 test/models/topodiff/test_topodiff.py diff --git a/examples/generative/topodiff/conf/config.yaml b/examples/generative/topodiff/conf/config.yaml new file mode 100644 index 0000000000..c4c70a6a8c --- /dev/null +++ b/examples/generative/topodiff/conf/config.yaml @@ -0,0 +1,32 @@ +# SPDX-FileCopyrightText: Copyright (c) 2023 - 2024 NVIDIA CORPORATION & AFFILIATES. +# SPDX-FileCopyrightText: All rights reserved. +# SPDX-License-Identifier: Apache-2.0 +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +hydra: + job: + chdir: True + run: + dir: ./outputs/ + + +path_data: "/home/turbo/ICMLCB/data/64/" + +epochs: 400000 +batch_size: 32 +lr: 1e-4 + +iterations: 400000 +diffusion_steps: 1000 +model_path: ./outputs/ diff --git a/examples/generative/topodiff/train.py b/examples/generative/topodiff/train.py new file mode 100644 index 0000000000..6fae3e618b --- /dev/null +++ b/examples/generative/topodiff/train.py @@ -0,0 +1,73 @@ +import torch +from torch.optim import AdamW +from tqdm import trange +import numpy as np +import time, os + + +import hydra +from hydra.utils import to_absolute_path +from omegaconf import DictConfig + +from modulus.models.topodiff import TopoDiff, Diffusion +from modulus.models.topodiff import UNetEncoder +from modulus.launch.logging import ( + PythonLogger, + initialize_wandb +) +from utils import load_data_topodiff + +@hydra.main(version_base="1.3", config_path="conf", config_name="config") +def main(cfg: DictConfig) -> None: + + logger = PythonLogger("main") # General Python Logger + logger.log("Start running") + logger.info("end running ") + topologies = np.load(cfg.path_data + "topologies.npy").astype(np.float64) + constraints = np.load(cfg.path_data + "constraints.npy", allow_pickle=True) + stress = np.load(cfg.path_data + "vonmises.npy", allow_pickle=True) + strain = np.load(cfg.path_data + "strain_energy.npy", allow_pickle=True) + load_imgs = np.load(cfg.path_data + "load_ims.npy") + labels = np.load(cfg.path_data+ "Floating/training_labels.npy").astype(np.float64) + + device = torch.device('cuda:0') + model = TopoDiff(64, 6, 1, model_channels=128, attn_resolutions=[16,8]).to(device) + diffusion = Diffusion(n_steps=1000,device=device) + + batch_size = cfg.batch_size + data = load_data_topodiff( + topologies, constraints, stress, strain, load_imgs, batch_size= batch_size,deterministic=False + ) + + lr = cfg.lr + optimizer = AdamW(model.parameters(), lr=lr) + + + prog = trange(cfg.epochs) + + for step in prog: + + tops, cons = next(data) + + tops = tops.float().to(device) + cons = cons.float().to(device) + + + losses = diffusion.train_loss(model, tops, cons) + + optimizer.zero_grad() + losses.backward() + optimizer.step() + + if step % 100 == 0: + print("epoch: %d, loss: %.5f" % (step, losses.item())) + torch.save(model.state_dict(), cfg.model_path + "model.pt") + print("job done!") + + +# ---------------------------------------------------------------------------- + +if __name__ == "__main__": + main() + +# ---------------------------------------------------------------------------- \ No newline at end of file diff --git a/examples/generative/topodiff/train_classifier.py b/examples/generative/topodiff/train_classifier.py new file mode 100644 index 0000000000..bf3d59c3ee --- /dev/null +++ b/examples/generative/topodiff/train_classifier.py @@ -0,0 +1,80 @@ +import torch +import torch.nn as nn +import torch.nn.functional as F +from torch.optim import AdamW +from torch.optim.lr_scheduler import LinearLR +from tqdm import trange +import numpy as np +import time, os + + + +import hydra +from hydra.utils import to_absolute_path +from omegaconf import DictConfig + +from modulus.models.topodiff import Diffusion +from modulus.models.topodiff import UNetEncoder +from modulus.launch.logging import ( + PythonLogger, + initialize_wandb +) +from utils import load_data_topodiff + +@hydra.main(version_base="1.3", config_path="conf", config_name="config") +def main(cfg: DictConfig) -> None: + + logger = PythonLogger("main") # General Python Logger + logger.log("Start running") + logger.info("end running ") + + train_img = np.load(cfg.path_data + "Floating/training.npy").astype(np.float64) + train_labels = np.load(cfg.path_data + "Floating/training_labels.npy").astype(np.float64) + + valid_img = np.load(cfg.path_data + "Floating/validation.npy").astype(np.float64) + valid_labels = np.load(cfg.path_data + "Floating/validation_labels.npy").astype(np.float64) + + device = torch.device('cuda:0') + + + classifier = UNetEncoder(in_channels = 1, out_channels=2).to(device) + + diffusion = Diffusion(n_steps=cfg.diffusion_steps,device=device) + print(cfg.model_path + "hello") + + batch_size = cfg.batch_size + + + lr = cfg.lr + optimizer = AdamW(classifier.parameters(), lr=lr) + scheduler = LinearLR(optimizer, start_factor=1, end_factor=0.001, total_iters=cfg.iterations) + + for i in range(cfg.iterations): + # get random batch from training data + idx = np.random.choice(len(train_img), batch_size, replace=False) + batch = torch.tensor(train_img[idx]).float().unsqueeze(1).to(device)*2-1 + batch_labels = torch.tensor(train_labels[idx]).long().to(device) + + t = torch.randint(0, cfg.diffusion_steps, (batch.shape[0], )).to(device) + batch = diffusion.q_sample(batch, t) + logits = classifier(batch,time_steps=t) + + loss = F.cross_entropy(logits,batch_labels) + optimizer.zero_grad() + loss.backward() + optimizer.step() + scheduler.step() + + if i % 100 == 0: + print("epoch: %d, loss: %.5f" % (i, loss.item())) + + torch.save(classifier.state_dict(), cfg.model_path + "regressor.pt") + print("job done!") + + +# ---------------------------------------------------------------------------- + +if __name__ == "__main__": + main() + +# ---------------------------------------------------------------------------- \ No newline at end of file diff --git a/examples/generative/topodiff/train_regressor.py b/examples/generative/topodiff/train_regressor.py new file mode 100644 index 0000000000..49237bc2f1 --- /dev/null +++ b/examples/generative/topodiff/train_regressor.py @@ -0,0 +1,107 @@ +import torch +import torch.nn as nn +from torch.optim import AdamW +from torch.optim.lr_scheduler import LinearLR +from tqdm import trange +import numpy as np +import time, os + + + +import hydra +from hydra.utils import to_absolute_path +from omegaconf import DictConfig + +from modulus.models.topodiff import Diffusion +from modulus.models.topodiff import UNetEncoder +from modulus.launch.logging import ( + PythonLogger, + initialize_wandb +) +from utils import load_data_topodiff + +@hydra.main(version_base="1.3", config_path="conf", config_name="config") +def main(cfg: DictConfig) -> None: + + logger = PythonLogger("main") # General Python Logger + logger.log("Start running") + logger.info("end running ") + + + topologies = np.load(cfg.path_data + "Compliance/Training/topologies.npy").astype(np.float64) + constraints = np.load(cfg.path_data + "Compliance/Training/constraints.npy", allow_pickle=True) + stress = np.load(cfg.path_data+ "Compliance/Training/vonmises.npy", allow_pickle=True) + strain = np.load(cfg.path_data + "Compliance/Training/strain_energy.npy", allow_pickle=True) + load_imgs = np.load(cfg.path_data + "Compliance/Training/load_imgs.npy") + bc_imgs = np.load(cfg.path_data + "Compliance/Training/bc_imgs.npy").astype(np.float64) + Compliance = np.load(cfg.path_data + "Compliance/Training/compliance.npy").astype(np.float64) + + + vfs = [] + for i in range(len(topologies)): + vfs.append(constraints[i]['VOL_FRAC']) + vfs = np.array(vfs) + + image_size = topologies.shape[-1] + + device = torch.device('cuda:0') + + in_channels = 1 + 3 + 2 + 2 + regressor = UNetEncoder(in_channels = in_channels, out_channels=1).to(device) + + diffusion = Diffusion(n_steps=cfg.diffusion_steps,device=device) + print(cfg.model_path + "hello") + + + + + batch_size = cfg.batch_size + + + lr = cfg.lr + optimizer = AdamW(regressor.parameters(), lr=lr) + scheduler = LinearLR(optimizer, start_factor=1, end_factor=0.001, total_iters=cfg.iterations) + + vf_in = torch.zeros([batch_size,1,image_size,image_size],dtype=torch.float32,device=device) + loss_fn = nn.MSELoss() + for i in range(cfg.iterations): + + # get random batch from training data + idx = np.random.choice(len(topologies), batch_size, replace=False) + batch = torch.tensor(topologies[idx]).float().unsqueeze(1).to(device)*2-1 + batch_stress = torch.tensor(stress[idx]).float().unsqueeze(1).to(device) + batch_strain = torch.tensor(strain[idx]).float().unsqueeze(1).to(device) + batch_load_imgs = torch.tensor(load_imgs[idx]).float().to(device).permute(0,3,1,2) + batch_bc_imgs = torch.tensor(bc_imgs[idx]).float().to(device).permute(0,3,1,2) + batch_vf = torch.tensor(vfs[idx]).float().to(device) + batch_labels = torch.tensor(Compliance[idx]).float().to(device).unsqueeze(1) + + vf_in = vf_in*0 + vf_in = vf_in + batch_vf.unsqueeze(1).unsqueeze(1).unsqueeze(1) + + t = torch.randint(0, cfg.diffusion_steps, (batch.shape[0], )).to(device) + batch = diffusion.q_sample(batch, t) + + batch = torch.cat((batch,batch_stress,batch_strain,batch_load_imgs,vf_in,batch_bc_imgs),dim=1) + + logits = regressor(batch,time_steps=t) + + loss = loss_fn(logits,batch_labels) + optimizer.zero_grad() + loss.backward() + optimizer.step() + scheduler.step() + + if i % 100 == 0: + print("epoch: %d, loss: %.5f" % (i, loss.item())) + + torch.save(regressor.state_dict(), cfg.model_path + "regressor.pt") + print("job done!") + + +# ---------------------------------------------------------------------------- + +if __name__ == "__main__": + main() + +# ---------------------------------------------------------------------------- \ No newline at end of file diff --git a/examples/generative/topodiff/utils.py b/examples/generative/topodiff/utils.py new file mode 100644 index 0000000000..10278657c9 --- /dev/null +++ b/examples/generative/topodiff/utils.py @@ -0,0 +1,116 @@ +# SPDX-FileCopyrightText: Copyright (c) 2023 - 2024 NVIDIA CORPORATION & AFFILIATES. +# SPDX-FileCopyrightText: All rights reserved. +# SPDX-License-Identifier: Apache-2.0 +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import numpy as np + +from torch.utils.data import Dataset, DataLoader + +""" +class DatasetTopoDiff(Dataset): + + def __init__(self, topologies, stress, strain, load_im, constraints): + + self.topologies = topologies + self.constraints = constraints + self.image_size = topologies.shape[1] + + self.stress = stress + self.strain = strain + self.load_im = load_im + + def __len__(self): + return self.topologies.shape[0] + + def __getitem__(self, idx): + + cons = self.constraints[idx] + + vol_frac = cons['VOL_FRAC'] + + cons = np.zeros((5, self.image_size, self.image_size)) + + cons[0] = self.stress[idx] + cons[1] = self.strain[idx] + cons[2] = self.load_im[idx][:,:,0] + cons[3] = self.load_im[idx][:,:,1] + cons[4] = np.ones((self.image_size,self.image_size)) * vol_frac + + return np.expand_dims(self.topologies[idx], 0) * 2 - 1, cons + +def load_data_topodiff(topologies, constraints, stress, strain, load_img, batch_size, deterministic=False): + dataset = DatasetTopoDiff(topologies, stress, strain, load_img, constraints) + if deterministic: + loader = DataLoader( + dataset, batch_size=batch_size, shuffle=False, num_workers=1, drop_last=True + ) + else: + loader = DataLoader( + dataset, batch_size=batch_size, shuffle=True, num_workers=1, drop_last=True + ) + + while True: + yield from loader +""" + +class DiffusionDataset_topodiff(Dataset): + def __init__(self, topologies, stress, strain, load_im, constraints): + + image_size = topologies.shape[1] + + self.topologies = topologies + self.constraints = constraints + self.image_size = image_size + self.stress = stress + self.strain = strain + self.load_im = load_im + + def __len__(self): + return self.topologies.shape[0] + + def __getitem__(self, idx): + + cons = self.constraints[idx] + + vol_frac = cons['VOL_FRAC'] + + cons = np.zeros((5, self.image_size, self.image_size)) + + cons[0] = self.stress[idx] + cons[1] = self.strain[idx] + cons[2] = self.load_im[idx][:,:,0] + cons[3] = self.load_im[idx][:,:,1] + cons[4] = np.ones((self.image_size, self.image_size)) * vol_frac + + return np.expand_dims(self.topologies[idx], 0) * 2 - 1, cons + +def load_data_topodiff( + topologies, constraints, stress, strain, load_img, batch_size, deterministic=False +): + + dataset = DiffusionDataset_topodiff( + topologies, stress, strain, load_img, constraints + ) + + if deterministic: + loader = DataLoader( + dataset, batch_size=batch_size, shuffle=False, num_workers=1, drop_last=True + ) + else: + loader = DataLoader( + dataset, batch_size=batch_size, shuffle=True, num_workers=1, drop_last=True + ) + while True: + yield from loader \ No newline at end of file diff --git a/modulus/models/topodiff/topodiff.py b/modulus/models/topodiff/topodiff.py index b37f2bd82e..1db8ba626e 100644 --- a/modulus/models/topodiff/topodiff.py +++ b/modulus/models/topodiff/topodiff.py @@ -252,7 +252,7 @@ def forward(self, x, cons, timesteps): x = self.out_conv(silu(self.out_norm(x))) return x -class UNetDecoder(Module): +class UNetEncoder(Module): def __init__( self, diff --git a/modulus/models/topodiff/train.py b/modulus/models/topodiff/train.py index 4524e9f5ce..e3e1f58e6f 100644 --- a/modulus/models/topodiff/train.py +++ b/modulus/models/topodiff/train.py @@ -24,7 +24,7 @@ from .topodiff import TopoDiff from .diffusion import Diffusion -from .utils import DatasetTopoDiff, load_data_topodiff +from .utils import load_data_topodiff if __name__ == "__main__": @@ -55,10 +55,18 @@ model = TopoDiff(64, 6, 1, model_channels=128, attn_resolutions=[16,8]).to(device) diffusion = Diffusion(n_steps=1000,device=device) + topologies = np.load(dataset_folder + "topologies.npy") + constraints = np.load(dataset_folder + "constraints.npy", allow_pickle=True) + stress = np.load(dataset_folder + "vonmises.npy", allow_pickle=True) + strain = np.load(dataset_folder + "strain_energy.npy", allow_pickle=True) + load_imgs = np.load(dataset_folder + "load_ims.npy") + img_size = topologies.shape[1] + batch_size = args.batch_size data = load_data_topodiff( topologies, constraints, stress, strain, load_imgs, batch_size= batch_size,deterministic=False ) + top, cons = next(data) lr = 1e-4 optimizer = AdamW(model.parameters(), lr=lr) diff --git a/test/models/topodiff/test_topodiff.py b/test/models/topodiff/test_topodiff.py new file mode 100644 index 0000000000..bc8a666192 --- /dev/null +++ b/test/models/topodiff/test_topodiff.py @@ -0,0 +1,57 @@ +# SPDX-FileCopyrightText: Copyright (c) 2023 - 2024 NVIDIA CORPORATION & AFFILIATES. +# SPDX-FileCopyrightText: All rights reserved. +# SPDX-License-Identifier: Apache-2.0 +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. +# ruff: noqa: E402 +import os +import random +import sys + +import numpy as np +import pytest +import torch + +script_path = os.path.abspath(__file__) +sys.path.append(os.path.join(os.path.dirname(script_path), "..")) + +import common +from pytest_utils import import_or_fail + +dgl = pytest.importorskip("dgl") + + +@import_or_fail("dgl") +@pytest.mark.parametrize("device", ["cuda:0", "cpu"]) +def test_topodiff_forward(device, pytestconfig): + """Test topodiff forward pass""" + + from modulus.models.topodiff import TopoDiff + + torch.manual_seed(0) + dgl.seed(0) + np.random.seed(0) + # Construct Topodiff Model + model = TopoDiff(img_resolution=64, + in_channels=6, + out_channels=1).to(device) + + bsize = 2 + nsteps = 1000 # diffusion steps + tops = torch.randn(bsize, 1, 64, 64).to(device) + cons = torch.randn(bsize, 5, 64, 64).to(device) + timesteps = torch.randint(0, nsteps, (bsize,)).to(device) + out = model(tops, cons, timesteps) + + assert out.shape == (bsize, 1, 64, 64) + From 679fb5c738378a82a9ce3c9655bf23f4c311bc23 Mon Sep 17 00:00:00 2001 From: FaezTurboGTS2000 Date: Thu, 26 Sep 2024 17:46:44 +0000 Subject: [PATCH 06/23] revise for 2nd round code review --- modulus/models/topodiff/train.py | 91 -------------------------------- 1 file changed, 91 deletions(-) delete mode 100644 modulus/models/topodiff/train.py diff --git a/modulus/models/topodiff/train.py b/modulus/models/topodiff/train.py deleted file mode 100644 index e3e1f58e6f..0000000000 --- a/modulus/models/topodiff/train.py +++ /dev/null @@ -1,91 +0,0 @@ -# SPDX-FileCopyrightText: Copyright (c) 2023 - 2024 NVIDIA CORPORATION & AFFILIATES. -# SPDX-FileCopyrightText: All rights reserved. -# SPDX-License-Identifier: Apache-2.0 -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - - -import torch -import torch.nn as nn -from torch.optim import AdamW -import argparse -from tqdm import trange -import numpy as np - -from .topodiff import TopoDiff -from .diffusion import Diffusion -from .utils import load_data_topodiff - -if __name__ == "__main__": - - - # Arguments - parser = argparse.ArgumentParser(description="Train the diffusion model") - parser.add_argument('--path_data', type=str, help='Path to the dataset folder') - parser.add_argument('--epochs', type=int, default=200000, help='Epoch number to train the model') - parser.add_argument('--batch_size', type=int, default=32, help='batch size to train the diffusion model') - - args = parser.parse_args() - - - print(args.path_data) - print(args.epochs) - - dataset_folder = args.path_data - - - topologies = np.load(dataset_folder + "topologies.npy").astype(np.float32) - constraints = np.load(dataset_folder + "constraints.npy", allow_pickle=True) - stress = np.load(dataset_folder + "vonmises.npy", allow_pickle=True) - strain = np.load(dataset_folder + "strain_energy.npy", allow_pickle=True) - load_imgs = np.load(dataset_folder + "load_ims.npy") - labels = np.load(dataset_folder + "Floating/training_labels.npy").astype(np.float32) - - device = torch.device('cuda:0') - model = TopoDiff(64, 6, 1, model_channels=128, attn_resolutions=[16,8]).to(device) - diffusion = Diffusion(n_steps=1000,device=device) - - topologies = np.load(dataset_folder + "topologies.npy") - constraints = np.load(dataset_folder + "constraints.npy", allow_pickle=True) - stress = np.load(dataset_folder + "vonmises.npy", allow_pickle=True) - strain = np.load(dataset_folder + "strain_energy.npy", allow_pickle=True) - load_imgs = np.load(dataset_folder + "load_ims.npy") - img_size = topologies.shape[1] - - batch_size = args.batch_size - data = load_data_topodiff( - topologies, constraints, stress, strain, load_imgs, batch_size= batch_size,deterministic=False - ) - top, cons = next(data) - - lr = 1e-4 - optimizer = AdamW(model.parameters(), lr=lr) - - prog = trange(args.epochs) - - for step in prog: - - tops, cons = next(data) - - tops = tops.float().to(device) - cons = cons.float().to(device) - - - losses = diffusion.train_loss(model, tops, cons) - - optimizer.zero_grad() - losses.backward() - optimizer.step() - - if step % 100 == 0: - print("epoch: %d, loss: %.5f" % (step, losses.item())) \ No newline at end of file From c64082b98ba0fc4499483d1237efda0ea7fbeebd Mon Sep 17 00:00:00 2001 From: FaezTurboGTS2000 Date: Thu, 17 Oct 2024 23:42:08 +0000 Subject: [PATCH 07/23] CHANGELOG update for TopoDiff --- CHANGELOG.md | 1 + 1 file changed, 1 insertion(+) diff --git a/CHANGELOG.md b/CHANGELOG.md index 964a981d86..4951dfde73 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -17,6 +17,7 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0 - Added gradient clipping to StaticCapture utilities. - Bistride Multiscale MeshGraphNet example. - FIGConvUNet model and example. +- TopoDiff model and example. ### Changed From 9ab35e55db17ffdd9b4383f9f588e59a3198e32a Mon Sep 17 00:00:00 2001 From: FaezTurboGTS2000 Date: Fri, 18 Oct 2024 00:22:28 +0000 Subject: [PATCH 08/23] code reivew for merge --- modulus/models/topodiff/topodiff.py | 12 +++++------- 1 file changed, 5 insertions(+), 7 deletions(-) diff --git a/modulus/models/topodiff/topodiff.py b/modulus/models/topodiff/topodiff.py index 1db8ba626e..3c6e90895a 100644 --- a/modulus/models/topodiff/topodiff.py +++ b/modulus/models/topodiff/topodiff.py @@ -42,18 +42,16 @@ class MetaData(ModelMetaData): name: str = "TopoDiff" # Optimization - jit: bool = False + jit: bool = True cuda_graphs: bool = False amp_cpu: bool = False amp_gpu: bool = True - torch_fx: bool = False - # Data type - bf16: bool = True # Inference - onnx: bool = False + onnx: bool = True # Physics informed - func_torch: bool = False - auto_grad: bool = False + var_dim: int = 1 + func_torch: bool = True + auto_grad: bool = True class TopoDiff(Module): From e123760b1652ea0cdba8e845bded486c2ec48ed2 Mon Sep 17 00:00:00 2001 From: FaezTurboGTS2000 Date: Fri, 18 Oct 2024 16:26:04 +0000 Subject: [PATCH 09/23] code review --- examples/generative/topodiff/conf/config.yaml | 1 + examples/generative/topodiff/inference.py | 71 +++++++++++++++++++ examples/generative/topodiff/train.py | 10 +-- 3 files changed, 77 insertions(+), 5 deletions(-) create mode 100644 examples/generative/topodiff/inference.py diff --git a/examples/generative/topodiff/conf/config.yaml b/examples/generative/topodiff/conf/config.yaml index c4c70a6a8c..4ee0b10fef 100644 --- a/examples/generative/topodiff/conf/config.yaml +++ b/examples/generative/topodiff/conf/config.yaml @@ -30,3 +30,4 @@ lr: 1e-4 iterations: 400000 diffusion_steps: 1000 model_path: ./outputs/ +generation_path: . diff --git a/examples/generative/topodiff/inference.py b/examples/generative/topodiff/inference.py new file mode 100644 index 0000000000..d42f6424d1 --- /dev/null +++ b/examples/generative/topodiff/inference.py @@ -0,0 +1,71 @@ +import torch +from torch.optim import AdamW +from tqdm import trange +import numpy as np +import time, os + + +import hydra +from hydra.utils import to_absolute_path +from omegaconf import DictConfig + +from modulus.models.topodiff import TopoDiff, Diffusion +from modulus.models.topodiff import UNetEncoder +from modulus.launch.logging import ( + PythonLogger, + initialize_wandb +) +from utils import load_data_topodiff + +@hydra.main(version_base="1.3", config_path="conf", config_name="config") +def main(cfg: DictConfig) -> None: + + logger = PythonLogger("main") # General Python Logger + logger.log("Job start!") + + topologies = np.load(cfg.path_data + "Test_InDistro/topologies.npy").astype(np.float64) + constraints = np.load(cfg.path_data + "Test_InDistro/constraints.npy", allow_pickle=True) + stress = np.load(cfg.path_data + "Test_InDistro/vonmises.npy", allow_pickle=True) + strain = np.load(cfg.path_data + "Test_InDistro/strain_energy.npy", allow_pickle=True) + load_imgs = np.load(cfg.path_data + "/Test_InDistro/load_ims.npy") + + + + device = torch.device('cuda:0') + model = TopoDiff(64, 6, 1, model_channels=128, attn_resolutions=[16,8]).to(device) + + diffusion = Diffusion(n_steps=1000,device=device) + batch_size = cfg.batch_size + data = load_data_topodiff( + topologies, constraints, stress, strain, load_imgs, batch_size= batch_size,deterministic=False + ) + + _, cons = next(data) + + cons = cons.float().to(device) + + n_steps = 1000 + batch_size = 32 + + xt = torch.randn(batch_size, 1, 64, 64).to(device) + + result = [] + with torch.no_grad(): + for i in reversed(range(n_steps)): + if i > 1: + z = torch.randn_like(xt).to(device) + else: + z = torch.zeros_like(xt).to(device) + t = torch.tensor([i] * batch_size, device = device) + noisy = diffusion.p_sample(model,xt, t, cons) + + xt = 1 / diffusion.alphas[i].sqrt() * (xt - noisy * (1 - diffusion.alphas[i])/(1 - diffusion.alpha_bars[i]).sqrt()) + + diffusion.betas[i].sqrt() * z + + result = xt.cpu().numpy() + + np.save(cfg.generation_path + 'results.npy', result) + + +if __name__ == "__main__": + main() + \ No newline at end of file diff --git a/examples/generative/topodiff/train.py b/examples/generative/topodiff/train.py index 6fae3e618b..f7b3c2f95f 100644 --- a/examples/generative/topodiff/train.py +++ b/examples/generative/topodiff/train.py @@ -21,8 +21,8 @@ def main(cfg: DictConfig) -> None: logger = PythonLogger("main") # General Python Logger - logger.log("Start running") - logger.info("end running ") + logger.log("Job start!") + topologies = np.load(cfg.path_data + "topologies.npy").astype(np.float64) constraints = np.load(cfg.path_data + "constraints.npy", allow_pickle=True) stress = np.load(cfg.path_data + "vonmises.npy", allow_pickle=True) @@ -41,7 +41,7 @@ def main(cfg: DictConfig) -> None: lr = cfg.lr optimizer = AdamW(model.parameters(), lr=lr) - + logger.log("Start training!") prog = trange(cfg.epochs) @@ -60,9 +60,9 @@ def main(cfg: DictConfig) -> None: optimizer.step() if step % 100 == 0: - print("epoch: %d, loss: %.5f" % (step, losses.item())) + logger.info("epoch: %d, loss: %.5f" % (step, losses.item())) torch.save(model.state_dict(), cfg.model_path + "model.pt") - print("job done!") + logger.info("Training completed!") # ---------------------------------------------------------------------------- From f7773df7d3bf6fe3926504863ebf17f33d00fa39 Mon Sep 17 00:00:00 2001 From: FaezTurboGTS2000 Date: Mon, 28 Oct 2024 20:14:23 +0000 Subject: [PATCH 10/23] add command to run the model --- docs/img/topodiff_doc/topology_generated.png | Bin 0 -> 24605 bytes examples/generative/topodiff/README.md | 20 ++++++++++++++++++ examples/generative/topodiff/conf/config.yaml | 7 +++--- examples/generative/topodiff/inference.py | 8 ++++--- examples/generative/topodiff/train.py | 3 ++- modulus/models/topodiff/topodiff.py | 6 +++--- 6 files changed, 34 insertions(+), 10 deletions(-) create mode 100644 docs/img/topodiff_doc/topology_generated.png diff --git a/docs/img/topodiff_doc/topology_generated.png b/docs/img/topodiff_doc/topology_generated.png new file mode 100644 index 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+ +### Model training +Run the following command to train the diffusion model +```Bash +python examples/generative/topodiff/train.py +``` +### Generation +Run the following command to generate topologies: +```Bash +python examples/generative/topodiff/inference.py +``` + + + ## Citations To cite this work, please use the following reference: diff --git a/examples/generative/topodiff/conf/config.yaml b/examples/generative/topodiff/conf/config.yaml index 4ee0b10fef..d4eb5ee284 100644 --- a/examples/generative/topodiff/conf/config.yaml +++ b/examples/generative/topodiff/conf/config.yaml @@ -24,10 +24,11 @@ hydra: path_data: "/home/turbo/ICMLCB/data/64/" epochs: 400000 -batch_size: 32 +batch_size: 64 lr: 1e-4 iterations: 400000 diffusion_steps: 1000 -model_path: ./outputs/ -generation_path: . +model_path: ./ +generation_path: ./ +model_path_inference: /home/turbo/Qian/modulus/modulus/outputs/model_300000.pt diff --git a/examples/generative/topodiff/inference.py b/examples/generative/topodiff/inference.py index d42f6424d1..08bef213f1 100644 --- a/examples/generative/topodiff/inference.py +++ b/examples/generative/topodiff/inference.py @@ -32,7 +32,9 @@ def main(cfg: DictConfig) -> None: device = torch.device('cuda:0') - model = TopoDiff(64, 6, 1, model_channels=128, attn_resolutions=[16,8]).to(device) + model = TopoDiff(64, 6, 1, model_channels=128, attn_resolutions=[16,8]) + model.load_state_dict(torch.load(cfg.model_path_inference)) + model.to(device) diffusion = Diffusion(n_steps=1000,device=device) batch_size = cfg.batch_size @@ -45,7 +47,7 @@ def main(cfg: DictConfig) -> None: cons = cons.float().to(device) n_steps = 1000 - batch_size = 32 + #batch_size = 32 xt = torch.randn(batch_size, 1, 64, 64).to(device) @@ -61,7 +63,7 @@ def main(cfg: DictConfig) -> None: xt = 1 / diffusion.alphas[i].sqrt() * (xt - noisy * (1 - diffusion.alphas[i])/(1 - diffusion.alpha_bars[i]).sqrt()) + + diffusion.betas[i].sqrt() * z - result = xt.cpu().numpy() + result = xt.cpu().detach().numpy() np.save(cfg.generation_path + 'results.npy', result) diff --git a/examples/generative/topodiff/train.py b/examples/generative/topodiff/train.py index f7b3c2f95f..d461677af5 100644 --- a/examples/generative/topodiff/train.py +++ b/examples/generative/topodiff/train.py @@ -61,7 +61,8 @@ def main(cfg: DictConfig) -> None: if step % 100 == 0: logger.info("epoch: %d, loss: %.5f" % (step, losses.item())) - torch.save(model.state_dict(), cfg.model_path + "model.pt") + if step % 100000 == 0: + torch.save(model.state_dict(), cfg.model_path + "model_" + str(step) + ".pt") logger.info("Training completed!") diff --git a/modulus/models/topodiff/topodiff.py b/modulus/models/topodiff/topodiff.py index 3c6e90895a..28952a517f 100644 --- a/modulus/models/topodiff/topodiff.py +++ b/modulus/models/topodiff/topodiff.py @@ -118,10 +118,10 @@ def __init__( label_dim: int = 0, augment_dim: int = 0, model_channels: int = 128, - channel_mult: List[int] = [1, 2, 3, 4], + channel_mult: List[int] = [1, 1, 1, 1], channel_mult_emb: int = 4, - num_blocks: int = 3, - attn_resolutions: List[int] = [32, 16, 8], + num_blocks: int = 2, + attn_resolutions: List[int] = [16, 8], dropout: float = 0.10, label_dropout: float = 0.0, ): From 9eeb0170e2700ed62933bf0c48d2c773c854ea85 Mon Sep 17 00:00:00 2001 From: FaezTurboGTS2000 Date: Mon, 28 Oct 2024 20:37:31 +0000 Subject: [PATCH 11/23] add command to run the model --- examples/generative/topodiff/README.md | 8 +++++--- 1 file changed, 5 insertions(+), 3 deletions(-) diff --git a/examples/generative/topodiff/README.md b/examples/generative/topodiff/README.md index 94547e20d1..657d339734 100644 --- a/examples/generative/topodiff/README.md +++ b/examples/generative/topodiff/README.md @@ -9,18 +9,20 @@ We propose Topodiff, a conditional diffusion-model-based architecture to perform ## Dataset Link to the dataset: [here](https://www.dropbox.com/home/decode_lab/Datasets/Public%20Documents/Topodiff_dataset) +Download the dataset and set the **path_data** in [text](conf/config.yaml) ## Instructions - - +2D topology structures could be generated by Topodiff conditioned on the boundary and loading conditions. A few examples are shown below:

### Model training -Run the following command to train the diffusion model +Run the following command to train the diffusion model, classifier for floating material and regressor for compliance: ```Bash python examples/generative/topodiff/train.py +python examples/generative/topodiff/train_classifier.py +python examples/generative/topodiff/train_regressor.py ``` ### Generation Run the following command to generate topologies: From 91a3ab6ea656d35d9d5aae18f5c46d4cde25bed7 Mon Sep 17 00:00:00 2001 From: FaezTurboGTS2000 Date: Mon, 28 Oct 2024 20:38:39 +0000 Subject: [PATCH 12/23] add command to run the model --- examples/generative/topodiff/README.md | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/examples/generative/topodiff/README.md b/examples/generative/topodiff/README.md index 657d339734..4b52bee83e 100644 --- a/examples/generative/topodiff/README.md +++ b/examples/generative/topodiff/README.md @@ -9,7 +9,8 @@ We propose Topodiff, a conditional diffusion-model-based architecture to perform ## Dataset Link to the dataset: [here](https://www.dropbox.com/home/decode_lab/Datasets/Public%20Documents/Topodiff_dataset) -Download the dataset and set the **path_data** in [text](conf/config.yaml) + +Download the dataset and set the **path_data** in [the config yaml file](conf/config.yaml) ## Instructions 2D topology structures could be generated by Topodiff conditioned on the boundary and loading conditions. A few examples are shown below: From c5555413a473f90017a89a4259e86457aa2e2d3c Mon Sep 17 00:00:00 2001 From: FaezTurboGTS2000 Date: Mon, 28 Oct 2024 21:11:27 +0000 Subject: [PATCH 13/23] add command to run the model --- examples/generative/topodiff/README.md | 1 + 1 file changed, 1 insertion(+) diff --git a/examples/generative/topodiff/README.md b/examples/generative/topodiff/README.md index 4b52bee83e..8fae2dbc93 100644 --- a/examples/generative/topodiff/README.md +++ b/examples/generative/topodiff/README.md @@ -26,6 +26,7 @@ python examples/generative/topodiff/train_classifier.py python examples/generative/topodiff/train_regressor.py ``` ### Generation +By default, the generated topologies are conditioned on the boundary and loading conditions that have not been seen in the training process. Run the following command to generate topologies: ```Bash python examples/generative/topodiff/inference.py From 776de9dc998f4dd7e81a57e5390f93df713580e2 Mon Sep 17 00:00:00 2001 From: FaezTurboGTS2000 Date: Tue, 29 Oct 2024 03:55:36 +0000 Subject: [PATCH 14/23] avoid floating material in generation --- examples/generative/topodiff/conf/config.yaml | 5 +- examples/generative/topodiff/inference.py | 61 ++++++++++++++++++- .../generative/topodiff/train_classifier.py | 28 +++++++-- 3 files changed, 85 insertions(+), 9 deletions(-) diff --git a/examples/generative/topodiff/conf/config.yaml b/examples/generative/topodiff/conf/config.yaml index d4eb5ee284..c6f092066d 100644 --- a/examples/generative/topodiff/conf/config.yaml +++ b/examples/generative/topodiff/conf/config.yaml @@ -28,7 +28,10 @@ batch_size: 64 lr: 1e-4 iterations: 400000 +classifier_iterations: 30000 +regressor_iterations: 100000 diffusion_steps: 1000 model_path: ./ generation_path: ./ -model_path_inference: /home/turbo/Qian/modulus/modulus/outputs/model_300000.pt +model_path_diffusion: /home/turbo/Qian/modulus/modulus/outputs/model_300000.pt +model_path_classifier: /home/turbo/Qian/modulus/modulus/outputs/classifier.pt diff --git a/examples/generative/topodiff/inference.py b/examples/generative/topodiff/inference.py index 08bef213f1..5ac593bdd6 100644 --- a/examples/generative/topodiff/inference.py +++ b/examples/generative/topodiff/inference.py @@ -1,8 +1,9 @@ import torch from torch.optim import AdamW +import torch.nn.functional as F from tqdm import trange import numpy as np -import time, os +import matplotlib.pyplot as plt import hydra @@ -33,9 +34,13 @@ def main(cfg: DictConfig) -> None: device = torch.device('cuda:0') model = TopoDiff(64, 6, 1, model_channels=128, attn_resolutions=[16,8]) - model.load_state_dict(torch.load(cfg.model_path_inference)) + model.load_state_dict(torch.load(cfg.model_path_diffusion)) model.to(device) + classifier = UNetEncoder(in_channels = 1, out_channels=2) + classifier.load_state_dict(torch.load(cfg.model_path_classifier)) + classifier.to(device) + diffusion = Diffusion(n_steps=1000,device=device) batch_size = cfg.batch_size data = load_data_topodiff( @@ -50,7 +55,33 @@ def main(cfg: DictConfig) -> None: #batch_size = 32 xt = torch.randn(batch_size, 1, 64, 64).to(device) + floating_labels = torch.tensor([0]*batch_size).long().to(device) + + for i in reversed(range(n_steps)): + with torch.no_grad(): + if i > 1: + z = torch.rand_like(xt).to(device) + else: + z = torch.zeros_like(xt).to(device) + t = torch.tensor([i] * batch_size, device = device) + noisy = diffusion.p_sample(model,xt, t, cons) + with torch.enable_grad(): + xt.requires_grad_(True) + logits = classifier(xt,time_steps=t) + loss = F.cross_entropy(logits,floating_labels) + + grad = torch.autograd.grad(loss, xt)[0] + + xt = 1 / diffusion.alphas[i].sqrt() * (xt - noisy * (1 - diffusion.alphas[i])/(1 - diffusion.alpha_bars[i]).sqrt()) + + if i > 0: + z = torch.zeros_like(xt).to(device) + xt = xt + diffusion.betas[i].sqrt() * (z * 0.8 + 0.2 * grad.float()) + + result = xt.cpu().detach().numpy() + + """ result = [] with torch.no_grad(): for i in reversed(range(n_steps)): @@ -60,13 +91,37 @@ def main(cfg: DictConfig) -> None: z = torch.zeros_like(xt).to(device) t = torch.tensor([i] * batch_size, device = device) noisy = diffusion.p_sample(model,xt, t, cons) + logits = classifier(batch_size,time_steps=t) + + loss = F.cross_entropy(logits,floating_labels) + + xt = 1 / diffusion.alphas[i].sqrt() * (xt - noisy * (1 - diffusion.alphas[i])/(1 - diffusion.alpha_bars[i]).sqrt()) + + diffusion.betas[i].sqrt() * z result = xt.cpu().detach().numpy() - + """ np.save(cfg.generation_path + 'results.npy', result) + # plot images for the generated samples + fig, axes = plt.subplots(2,4, figsize=(12,6),dpi=300) + + for i in range(2): + for j in range(4): + img = np.zeros((64,64)) + img[result[i*4 + j ][0] > 0] = 1 + axes[i,j].imshow(img, cmap='gray') + axes[i,j].axis('off') + + + plt.xticks([]) # Remove x-axis ticks + plt.yticks([]) # Remove y-axis ticks + plt.gca().xaxis.set_visible(False) # Optionally hide x-axis + plt.gca().yaxis.set_visible(False) # Optionally hide y-axis + + plt.savefig(cfg.generation_path + 'grid.png', bbox_inches='tight', pad_inches=0) + + if __name__ == "__main__": main() diff --git a/examples/generative/topodiff/train_classifier.py b/examples/generative/topodiff/train_classifier.py index bf3d59c3ee..1480c46fb4 100644 --- a/examples/generative/topodiff/train_classifier.py +++ b/examples/generative/topodiff/train_classifier.py @@ -40,7 +40,6 @@ def main(cfg: DictConfig) -> None: classifier = UNetEncoder(in_channels = 1, out_channels=2).to(device) diffusion = Diffusion(n_steps=cfg.diffusion_steps,device=device) - print(cfg.model_path + "hello") batch_size = cfg.batch_size @@ -49,8 +48,9 @@ def main(cfg: DictConfig) -> None: optimizer = AdamW(classifier.parameters(), lr=lr) scheduler = LinearLR(optimizer, start_factor=1, end_factor=0.001, total_iters=cfg.iterations) - for i in range(cfg.iterations): + for i in range(cfg.classifier_iterations): # get random batch from training data + idx = np.random.choice(len(train_img), batch_size, replace=False) batch = torch.tensor(train_img[idx]).float().unsqueeze(1).to(device)*2-1 batch_labels = torch.tensor(train_labels[idx]).long().to(device) @@ -66,9 +66,27 @@ def main(cfg: DictConfig) -> None: scheduler.step() if i % 100 == 0: - print("epoch: %d, loss: %.5f" % (i, loss.item())) - - torch.save(classifier.state_dict(), cfg.model_path + "regressor.pt") + with torch.no_grad(): + idx = np.random.choice(len(valid_img), batch_size, replace=False) + batch = torch.tensor(valid_img[idx]).float().unsqueeze(1).to(device) * 2 - 1 + batch_labels = torch.tensor(valid_labels[idx]).long().to(device) + + # Sample diffusion steps and get noised images + t = torch.randint(0, cfg.diffusion_steps, (batch.shape[0], )).to(device) + batch = diffusion.q_sample(batch, t) + + # Forward pass + logits = classifier(batch, time_steps=t) + + # Compute accuracy + predicted_labels = torch.argmax(logits, dim=1) + correct_predictions = (predicted_labels == batch_labels).sum().item() + accuracy = correct_predictions / batch_size + + print("epoch: %d, loss: %.5f, validation accuracy: %.3f" % (i, loss.item(), accuracy)) + if i % 10000 == 0: + torch.save(classifier.state_dict(), cfg.model_path + "classifier.pt") + print("job done!") 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test/models/data/topodiff_output.pth diff --git a/examples/generative/topodiff/inference.py b/examples/generative/topodiff/inference.py index 5ac593bdd6..55d72ea37d 100644 --- a/examples/generative/topodiff/inference.py +++ b/examples/generative/topodiff/inference.py @@ -59,12 +59,9 @@ def main(cfg: DictConfig) -> None: for i in reversed(range(n_steps)): with torch.no_grad(): - if i > 1: - z = torch.rand_like(xt).to(device) - else: - z = torch.zeros_like(xt).to(device) t = torch.tensor([i] * batch_size, device = device) noisy = diffusion.p_sample(model,xt, t, cons) + with torch.enable_grad(): xt.requires_grad_(True) logits = classifier(xt,time_steps=t) diff --git a/modulus/models/topodiff/topodiff.py b/modulus/models/topodiff/topodiff.py index 28952a517f..a4d28eddc3 100644 --- a/modulus/models/topodiff/topodiff.py +++ b/modulus/models/topodiff/topodiff.py @@ -42,7 +42,7 @@ class MetaData(ModelMetaData): name: str = "TopoDiff" # Optimization - jit: bool = True + jit: bool = False cuda_graphs: bool = False amp_cpu: bool = False amp_gpu: bool = True @@ -50,8 +50,8 @@ class MetaData(ModelMetaData): onnx: bool = True # Physics informed var_dim: int = 1 - func_torch: bool = True - auto_grad: bool = True + func_torch: bool = False + auto_grad: bool = False class TopoDiff(Module): diff --git a/test/models/data/topodiff_output.pth b/test/models/data/topodiff_output.pth new file mode 100644 index 0000000000000000000000000000000000000000..be585f52a59618056c8f34d06e2d8d67de0a8f46 GIT binary patch literal 66756 zcmeIwJ#W)M9LMo<9*yb1)HVxal_E&ohdflMQWc>rRz)ZRRaqh@c3M-bj-5-TA|bJK zU}0tB9rSH5b%2Q%(2;?a1&O;RR82yqiuwCbF3u(Apa1@xTTVJ7glUSoS{9a&(IE1I ze!mlq8N;)U& zR%{fD`+0Sdp<3x4_;G4{iq#AwSH2woXvOJ$L#?sn%zj!OdxKG&-4#1B&b@F%(aAW5 zv*V~~zPKwMsJEIKGB!W-dQlkqJsCvDL+g3s`HAm63wqLu@2&2|mW=Q3#S6ny%gLMF z)eGlbW$V$p_2vs=VeaO;r_zsfcC%ql?!Mg)11Upa2cK!r7o$*?H#O$M{^yNJXgBqp`Qe97`%0*>ZTKT&Bn?d z8?r04YQ0jgS85ye%GOqOy;fhhRjt{Z)ve0r+D65;YxR0fclq=1i&0b^>d|rx-R!kg zclE+KS93j<^&e9;>!zD74UWRul(X&2LQb8jM`0@Ax0=nSbWf!pC7z$m#@yV{#i^Lr zYL{nYt_1DbjGJ4sG?nqyFy=BQ55jE7O|g}!kiFD=$iMfp<>YdYC+9v)Ke Date: Tue, 29 Oct 2024 18:29:56 +0000 Subject: [PATCH 17/23] topodiff merge --- examples/generative/topodiff/README.md | 30 ++++++++++++++++++- examples/generative/topodiff/conf/config.yaml | 7 ++--- .../generative/topodiff/train_regressor.py | 2 +- 3 files changed, 33 insertions(+), 6 deletions(-) diff --git a/examples/generative/topodiff/README.md b/examples/generative/topodiff/README.md index 8fae2dbc93..8dd595110c 100644 --- a/examples/generative/topodiff/README.md +++ b/examples/generative/topodiff/README.md @@ -19,6 +19,30 @@ Download the dataset and set the **path_data** in [the config yaml file](conf/co

### Model training +Before training the model, config.yaml should be: +``` +hydra: + job: + chdir: True + run: + dir: ./outputs/ + + +path_data: path to the Topodiff dataset downloaded + +epochs: 200000 +batch_size: 128 +lr: 1e-4 + +classifier_iterations: 30000 +regressor_iterations: 30000 +diffusion_steps: 1000 + +generation_path: ./ +model_path_diffusion: /home/turbo/Qian/modulus/modulus/outputs/model_300000.pt +model_path_classifier: /home/turbo/Qian/modulus/modulus/outputs/classifier.pt +``` + Run the following command to train the diffusion model, classifier for floating material and regressor for compliance: ```Bash python examples/generative/topodiff/train.py @@ -50,5 +74,9 @@ To cite this work, please use the following reference: } ``` -## Contacts +## References +- [Diffusion Models Beat GANs on Topology Optimization](https://decode.mit.edu/assets/papers/2022_maze_topodiff.pdf) +- [Topodiff Project Page](https://decode.mit.edu/projects/topodiff/) +- [Topodiff Dataset](https://www.dropbox.com/home/decode_lab/Datasets/Public%20Documents/Topodiff_dataset) +- [Github](https://github.com/francoismaze/topodiff) diff --git a/examples/generative/topodiff/conf/config.yaml b/examples/generative/topodiff/conf/config.yaml index c6f092066d..b171f62e71 100644 --- a/examples/generative/topodiff/conf/config.yaml +++ b/examples/generative/topodiff/conf/config.yaml @@ -23,13 +23,12 @@ hydra: path_data: "/home/turbo/ICMLCB/data/64/" -epochs: 400000 -batch_size: 64 +epochs: 200000 +batch_size: 128 lr: 1e-4 -iterations: 400000 classifier_iterations: 30000 -regressor_iterations: 100000 +regressor_iterations: 30000 diffusion_steps: 1000 model_path: ./ generation_path: ./ diff --git a/examples/generative/topodiff/train_regressor.py b/examples/generative/topodiff/train_regressor.py index 49237bc2f1..4c5e95a244 100644 --- a/examples/generative/topodiff/train_regressor.py +++ b/examples/generative/topodiff/train_regressor.py @@ -64,7 +64,7 @@ def main(cfg: DictConfig) -> None: vf_in = torch.zeros([batch_size,1,image_size,image_size],dtype=torch.float32,device=device) loss_fn = nn.MSELoss() - for i in range(cfg.iterations): + for i in range(cfg.regressor_iterations): # get random batch from training data idx = np.random.choice(len(topologies), batch_size, replace=False) From a28afc7e447a3af02de3c2cecbddde7c97ae72a9 Mon Sep 17 00:00:00 2001 From: FaezTurboGTS2000 Date: Tue, 29 Oct 2024 18:31:29 +0000 Subject: [PATCH 18/23] topodiff merge --- examples/generative/topodiff/README.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/examples/generative/topodiff/README.md b/examples/generative/topodiff/README.md index 8dd595110c..310699aac2 100644 --- a/examples/generative/topodiff/README.md +++ b/examples/generative/topodiff/README.md @@ -39,8 +39,8 @@ regressor_iterations: 30000 diffusion_steps: 1000 generation_path: ./ -model_path_diffusion: /home/turbo/Qian/modulus/modulus/outputs/model_300000.pt -model_path_classifier: /home/turbo/Qian/modulus/modulus/outputs/classifier.pt +model_path_diffusion: path to the pt file of the diffusion model +model_path_classifier: path to the pt file of the classifier for floating material ``` Run the following command to train the diffusion model, classifier for floating material and regressor for compliance: From 0031dff5f5d50d2403909d285fe7ad5c19e290db Mon Sep 17 00:00:00 2001 From: FaezTurboGTS2000 Date: Tue, 29 Oct 2024 18:44:44 +0000 Subject: [PATCH 19/23] topodiff merge --- examples/generative/topodiff/train_regressor.py | 3 --- 1 file changed, 3 deletions(-) diff --git a/examples/generative/topodiff/train_regressor.py b/examples/generative/topodiff/train_regressor.py index 4c5e95a244..c769ff7516 100644 --- a/examples/generative/topodiff/train_regressor.py +++ b/examples/generative/topodiff/train_regressor.py @@ -50,9 +50,6 @@ def main(cfg: DictConfig) -> None: regressor = UNetEncoder(in_channels = in_channels, out_channels=1).to(device) diffusion = Diffusion(n_steps=cfg.diffusion_steps,device=device) - print(cfg.model_path + "hello") - - batch_size = cfg.batch_size From 5c8cc0116f10c755dd34a282c9ec085216f3b7fe Mon Sep 17 00:00:00 2001 From: FaezTurboGTS2000 Date: Thu, 31 Oct 2024 23:50:51 +0000 Subject: [PATCH 20/23] Topodiff merge --- docs/img/topodiff_doc/grid_topology.png | Bin 0 -> 263614 bytes docs/img/topodiff_doc/topology_generated.png | Bin 66901 -> 224280 bytes examples/generative/topodiff/conf/config.yaml | 23 ++++- examples/generative/topodiff/inference.py | 65 ++++--------- examples/generative/topodiff/train.py | 20 ++-- .../generative/topodiff/train_classifier.py | 29 +++--- .../generative/topodiff/train_regressor.py | 51 +++++----- examples/generative/topodiff/utils.py | 90 ++++++++++++++---- 8 files changed, 162 insertions(+), 116 deletions(-) create mode 100644 docs/img/topodiff_doc/grid_topology.png diff --git a/docs/img/topodiff_doc/grid_topology.png b/docs/img/topodiff_doc/grid_topology.png new file mode 100644 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zOlrVwkWCE?W_>5GEBfp6f7x0nzl1i3;~YQ^_mM;j#vsog*6DXz(@5joo*8 z9i=@h8DT2C$L6~rgGMT~&-ZrDWY6&Nw71^DP2Fq_-0X==6BTq=*A1p7h@mQgtvRx1z-HT1*gpxiKgTe?jMwFR}J)AJJ z_=rbwH+oHWa}mfWLw)Y}#`#ubX}N;k-nS~aQ&r}_uLlu`pSIODk$}mVNWO^&d02># zJ=J=@si|iVpU@y!2mS}IxNd9-sXK+cjoUArimXnRfd7j`mYx(}XFHo?q-d;|I|qtl z4tW4*Wn^uYc1H^KH`Z3L6jWAsGNvy<;oF(IK9E4-C$S&}ot2y#id3f09**9}1o6Gw z{)fyo;s=*ECeem`1Xv2kFP7jN{^1$?Uycm@XKN-qKc|*IsR0PbFhN7DJ&4qCJg%}4qxhhJNf0;*F}Dr@W9@mJqAIlj~ssb;Q+M11Y7d!3}gnMgymbPKFqdjTG3!5M4V&UtPyfz~&g9%!uLN;8i3ucjYY-R- z$2rOs55e#2D>F=o!NBC9BhVHuS=umRZu*ZJ$N%qr$+fQ_JEy!%Ah@-orcl-lz*Bpv z3s)J(-qQU5jgWvhM=H6yyK5G!4>PX@5^54ZS fOgY8}u~4zykA-Uke({lCj>FG;ej@$o^UMDM_o None: @@ -24,15 +24,12 @@ def main(cfg: DictConfig) -> None: logger = PythonLogger("main") # General Python Logger logger.log("Job start!") - topologies = np.load(cfg.path_data + "Test_InDistro/topologies.npy").astype(np.float64) - constraints = np.load(cfg.path_data + "Test_InDistro/constraints.npy", allow_pickle=True) - stress = np.load(cfg.path_data + "Test_InDistro/vonmises.npy", allow_pickle=True) - strain = np.load(cfg.path_data + "Test_InDistro/strain_energy.npy", allow_pickle=True) - load_imgs = np.load(cfg.path_data + "/Test_InDistro/load_ims.npy") - - + + topologies = np.random.randn(1800, 64,64) + vfs_stress_strain = load_data('/home/turbo/Qian/dataset_1_diff/test_data_level_1/',cfg.prefix_pf_file, '.npy', 200,2000) + load_imgs = load_data('/home/turbo/Qian/dataset_1_diff/test_data_level_1/', cfg.prefix_load_file, '.npy', 200,2000) - device = torch.device('cuda:0') + device = torch.device('cuda:1') model = TopoDiff(64, 6, 1, model_channels=128, attn_resolutions=[16,8]) model.load_state_dict(torch.load(cfg.model_path_diffusion)) model.to(device) @@ -44,20 +41,19 @@ def main(cfg: DictConfig) -> None: diffusion = Diffusion(n_steps=1000,device=device) batch_size = cfg.batch_size data = load_data_topodiff( - topologies, constraints, stress, strain, load_imgs, batch_size= batch_size,deterministic=False + topologies, vfs_stress_strain, load_imgs, batch_size= batch_size,deterministic=False ) _, cons = next(data) cons = cons.float().to(device) - n_steps = 1000 - #batch_size = 32 - + n_steps = 1000 + xt = torch.randn(batch_size, 1, 64, 64).to(device) - floating_labels = torch.tensor([0]*batch_size).long().to(device) + floating_labels = torch.tensor([1]*batch_size).long().to(device) - for i in reversed(range(n_steps)): + for i in reversed(trange(n_steps)): with torch.no_grad(): t = torch.tensor([i] * batch_size, device = device) noisy = diffusion.p_sample(model,xt, t, cons) @@ -75,40 +71,19 @@ def main(cfg: DictConfig) -> None: z = torch.zeros_like(xt).to(device) xt = xt + diffusion.betas[i].sqrt() * (z * 0.8 + 0.2 * grad.float()) - result = xt.cpu().detach().numpy() - - - """ - result = [] - with torch.no_grad(): - for i in reversed(range(n_steps)): - if i > 1: - z = torch.randn_like(xt).to(device) - else: - z = torch.zeros_like(xt).to(device) - t = torch.tensor([i] * batch_size, device = device) - noisy = diffusion.p_sample(model,xt, t, cons) - logits = classifier(batch_size,time_steps=t) - - loss = F.cross_entropy(logits,floating_labels) - - + result = (xt.cpu().detach().numpy() + 1) * 2 - xt = 1 / diffusion.alphas[i].sqrt() * (xt - noisy * (1 - diffusion.alphas[i])/(1 - diffusion.alpha_bars[i]).sqrt()) + + diffusion.betas[i].sqrt() * z - - result = xt.cpu().detach().numpy() - """ - np.save(cfg.generation_path + 'results.npy', result) + np.save(cfg.generation_path + 'results_topology.npy', result) # plot images for the generated samples - fig, axes = plt.subplots(2,4, figsize=(12,6),dpi=300) + fig, axes = plt.subplots(8,8, figsize=(12,6),dpi=300) - for i in range(2): - for j in range(4): - img = np.zeros((64,64)) - img[result[i*4 + j ][0] > 0] = 1 + for i in range(8): + for j in range(8): + img = result[i*4 + j ][0] axes[i,j].imshow(img, cmap='gray') - axes[i,j].axis('off') + axes[i,j].set_xticks([]) + axes[i,j].set_yticks([]) plt.xticks([]) # Remove x-axis ticks @@ -116,7 +91,7 @@ def main(cfg: DictConfig) -> None: plt.gca().xaxis.set_visible(False) # Optionally hide x-axis plt.gca().yaxis.set_visible(False) # Optionally hide y-axis - plt.savefig(cfg.generation_path + 'grid.png', bbox_inches='tight', pad_inches=0) + plt.savefig(cfg.generation_path + 'grid_topology.png', bbox_inches='tight', pad_inches=0) diff --git a/examples/generative/topodiff/train.py b/examples/generative/topodiff/train.py index d461677af5..f469015f6d 100644 --- a/examples/generative/topodiff/train.py +++ b/examples/generative/topodiff/train.py @@ -15,29 +15,35 @@ PythonLogger, initialize_wandb ) -from utils import load_data_topodiff +from utils import load_data_topodiff, load_data @hydra.main(version_base="1.3", config_path="conf", config_name="config") def main(cfg: DictConfig) -> None: logger = PythonLogger("main") # General Python Logger logger.log("Job start!") - + """ topologies = np.load(cfg.path_data + "topologies.npy").astype(np.float64) constraints = np.load(cfg.path_data + "constraints.npy", allow_pickle=True) stress = np.load(cfg.path_data + "vonmises.npy", allow_pickle=True) strain = np.load(cfg.path_data + "strain_energy.npy", allow_pickle=True) load_imgs = np.load(cfg.path_data + "load_ims.npy") labels = np.load(cfg.path_data+ "Floating/training_labels.npy").astype(np.float64) - + """ device = torch.device('cuda:0') model = TopoDiff(64, 6, 1, model_channels=128, attn_resolutions=[16,8]).to(device) diffusion = Diffusion(n_steps=1000,device=device) - + + + topologies = load_data(cfg.path_training_data_diffusion, cfg.prefix_topology_file, '.png', 0,30000) + vfs_stress_strain = load_data(cfg.path_training_data_diffusion,cfg.prefix_pf_file, '.npy', 0,30000) + load_imgs = load_data(cfg.path_training_data_diffusion, cfg.prefix_load_file, '.npy', 0,30000) + batch_size = cfg.batch_size data = load_data_topodiff( - topologies, constraints, stress, strain, load_imgs, batch_size= batch_size,deterministic=False + topologies, vfs_stress_strain, load_imgs, batch_size= batch_size,deterministic=False ) + print("the min and max of topologies: ", topologies.min(), topologies.max()) lr = cfg.lr optimizer = AdamW(model.parameters(), lr=lr) @@ -61,8 +67,8 @@ def main(cfg: DictConfig) -> None: if step % 100 == 0: logger.info("epoch: %d, loss: %.5f" % (step, losses.item())) - if step % 100000 == 0: - torch.save(model.state_dict(), cfg.model_path + "model_" + str(step) + ".pt") + + torch.save(model.state_dict(), cfg.model_path + "topodiff_model.pt") logger.info("Training completed!") diff --git a/examples/generative/topodiff/train_classifier.py b/examples/generative/topodiff/train_classifier.py index 1480c46fb4..27ec663e03 100644 --- a/examples/generative/topodiff/train_classifier.py +++ b/examples/generative/topodiff/train_classifier.py @@ -19,26 +19,31 @@ PythonLogger, initialize_wandb ) -from utils import load_data_topodiff +from utils import load_data_topodiff, load_data_classifier @hydra.main(version_base="1.3", config_path="conf", config_name="config") def main(cfg: DictConfig) -> None: logger = PythonLogger("main") # General Python Logger logger.log("Start running") - logger.info("end running ") - - train_img = np.load(cfg.path_data + "Floating/training.npy").astype(np.float64) - train_labels = np.load(cfg.path_data + "Floating/training_labels.npy").astype(np.float64) - valid_img = np.load(cfg.path_data + "Floating/validation.npy").astype(np.float64) - valid_labels = np.load(cfg.path_data + "Floating/validation_labels.npy").astype(np.float64) + #train_img = np.load(cfg.path_data + "Floating/training.npy").astype(np.float64) + #train_labels = np.load(cfg.path_data + "Floating/training_labels.npy").astype(np.float64) + + #valid_img = np.load(cfg.path_data + "Floating/validation.npy").astype(np.float64) + #valid_labels = np.load(cfg.path_data + "Floating/validation_labels.npy").astype(np.float64) - device = torch.device('cuda:0') + train_img, train_labels = load_data_classifier(cfg.path_data_classifier_training) + valid_img, valid_labels = load_data_classifier(cfg.path_data_classifier_validation) + train_img = 2 * train_img - 1 + valid_img = 2 * valid_img - 1 + print(train_img.min(), train_img.max()) + print(valid_img.min(), valid_img.max()) + device = torch.device('cuda:1') classifier = UNetEncoder(in_channels = 1, out_channels=2).to(device) - + classifier.load_state_dict(torch.load('/home/turbo/Qian/modulus/modulus/outputs/classifier.pt')) diffusion = Diffusion(n_steps=cfg.diffusion_steps,device=device) batch_size = cfg.batch_size @@ -46,7 +51,7 @@ def main(cfg: DictConfig) -> None: lr = cfg.lr optimizer = AdamW(classifier.parameters(), lr=lr) - scheduler = LinearLR(optimizer, start_factor=1, end_factor=0.001, total_iters=cfg.iterations) + scheduler = LinearLR(optimizer, start_factor=1, end_factor=0.001, total_iters=cfg.classifier_iterations) for i in range(cfg.classifier_iterations): # get random batch from training data @@ -84,8 +89,8 @@ def main(cfg: DictConfig) -> None: accuracy = correct_predictions / batch_size print("epoch: %d, loss: %.5f, validation accuracy: %.3f" % (i, loss.item(), accuracy)) - if i % 10000 == 0: - torch.save(classifier.state_dict(), cfg.model_path + "classifier.pt") + + torch.save(classifier.state_dict(), cfg.model_path + "classifier.pt") print("job done!") diff --git a/examples/generative/topodiff/train_regressor.py b/examples/generative/topodiff/train_regressor.py index c769ff7516..375ff4ea2b 100644 --- a/examples/generative/topodiff/train_regressor.py +++ b/examples/generative/topodiff/train_regressor.py @@ -18,16 +18,17 @@ PythonLogger, initialize_wandb ) -from utils import load_data_topodiff +from utils import load_data_topodiff, load_data_regressor @hydra.main(version_base="1.3", config_path="conf", config_name="config") def main(cfg: DictConfig) -> None: logger = PythonLogger("main") # General Python Logger logger.log("Start running") - logger.info("end running ") - - + + topologies, load_imgs, vfs_stress_strain, labels = load_data_regressor(cfg.path_data_regressor_training) + topologies = topologies*2 - 1 # Normalize the range of image to be [-1, 1] + """ topologies = np.load(cfg.path_data + "Compliance/Training/topologies.npy").astype(np.float64) constraints = np.load(cfg.path_data + "Compliance/Training/constraints.npy", allow_pickle=True) stress = np.load(cfg.path_data+ "Compliance/Training/vonmises.npy", allow_pickle=True) @@ -35,51 +36,43 @@ def main(cfg: DictConfig) -> None: load_imgs = np.load(cfg.path_data + "Compliance/Training/load_imgs.npy") bc_imgs = np.load(cfg.path_data + "Compliance/Training/bc_imgs.npy").astype(np.float64) Compliance = np.load(cfg.path_data + "Compliance/Training/compliance.npy").astype(np.float64) + """ - - vfs = [] - for i in range(len(topologies)): - vfs.append(constraints[i]['VOL_FRAC']) - vfs = np.array(vfs) - - image_size = topologies.shape[-1] - device = torch.device('cuda:0') - in_channels = 1 + 3 + 2 + 2 + in_channels = 6 regressor = UNetEncoder(in_channels = in_channels, out_channels=1).to(device) diffusion = Diffusion(n_steps=cfg.diffusion_steps,device=device) batch_size = cfg.batch_size - - + """ + data = load_data_topodiff( + topologies, vfs_stress_strain, load_imgs, batch_size= batch_size,deterministic=False + ) + """ lr = cfg.lr optimizer = AdamW(regressor.parameters(), lr=lr) - scheduler = LinearLR(optimizer, start_factor=1, end_factor=0.001, total_iters=cfg.iterations) + scheduler = LinearLR(optimizer, start_factor=1, end_factor=0.001, total_iters=cfg.regressor_iterations) - vf_in = torch.zeros([batch_size,1,image_size,image_size],dtype=torch.float32,device=device) loss_fn = nn.MSELoss() for i in range(cfg.regressor_iterations): # get random batch from training data idx = np.random.choice(len(topologies), batch_size, replace=False) - batch = torch.tensor(topologies[idx]).float().unsqueeze(1).to(device)*2-1 - batch_stress = torch.tensor(stress[idx]).float().unsqueeze(1).to(device) - batch_strain = torch.tensor(strain[idx]).float().unsqueeze(1).to(device) - batch_load_imgs = torch.tensor(load_imgs[idx]).float().to(device).permute(0,3,1,2) - batch_bc_imgs = torch.tensor(bc_imgs[idx]).float().to(device).permute(0,3,1,2) - batch_vf = torch.tensor(vfs[idx]).float().to(device) - batch_labels = torch.tensor(Compliance[idx]).float().to(device).unsqueeze(1) + batch = torch.tensor(topologies[idx]).float().unsqueeze(1).to(device)*2-1 # 4 x 1 x 64 x 64 + batch_pf = torch.tensor(vfs_stress_strain[idx]).float().permute(0,3,1,2).to(device)*2-1 + batch_load = torch.tensor(load_imgs[idx]).float().permute(0,3,1,2).to(device) + + batch_labels = torch.tensor(labels[idx]).float().to(device).unsqueeze(1) - vf_in = vf_in*0 - vf_in = vf_in + batch_vf.unsqueeze(1).unsqueeze(1).unsqueeze(1) + t = torch.randint(0, cfg.diffusion_steps, (batch.shape[0], )).to(device) batch = diffusion.q_sample(batch, t) - batch = torch.cat((batch,batch_stress,batch_strain,batch_load_imgs,vf_in,batch_bc_imgs),dim=1) + batch = torch.cat((batch,batch_pf,batch_load),dim=1) logits = regressor(batch,time_steps=t) @@ -91,8 +84,8 @@ def main(cfg: DictConfig) -> None: if i % 100 == 0: print("epoch: %d, loss: %.5f" % (i, loss.item())) - - torch.save(regressor.state_dict(), cfg.model_path + "regressor.pt") + if i % 10000 == 0: + torch.save(regressor.state_dict(), cfg.model_path + "regressor.pt") print("job done!") diff --git a/examples/generative/topodiff/utils.py b/examples/generative/topodiff/utils.py index 10278657c9..59ce01f967 100644 --- a/examples/generative/topodiff/utils.py +++ b/examples/generative/topodiff/utils.py @@ -15,8 +15,9 @@ # limitations under the License. import numpy as np - +from PIL import Image from torch.utils.data import Dataset, DataLoader +import os """ class DatasetTopoDiff(Dataset): @@ -66,15 +67,13 @@ def load_data_topodiff(topologies, constraints, stress, strain, load_img, batch_ """ class DiffusionDataset_topodiff(Dataset): - def __init__(self, topologies, stress, strain, load_im, constraints): + def __init__(self, topologies, vfs_stress_strain, load_im): image_size = topologies.shape[1] self.topologies = topologies - self.constraints = constraints + self.vfs_stress_strain = vfs_stress_strain self.image_size = image_size - self.stress = stress - self.strain = strain self.load_im = load_im def __len__(self): @@ -82,26 +81,22 @@ def __len__(self): def __getitem__(self, idx): - cons = self.constraints[idx] - - vol_frac = cons['VOL_FRAC'] - cons = np.zeros((5, self.image_size, self.image_size)) - cons[0] = self.stress[idx] - cons[1] = self.strain[idx] - cons[2] = self.load_im[idx][:,:,0] - cons[3] = self.load_im[idx][:,:,1] - cons[4] = np.ones((self.image_size, self.image_size)) * vol_frac - + cons[0] = self.vfs_stress_strain[idx][:,:,0] + cons[1] = self.vfs_stress_strain[idx][:,:,1] + cons[2] = self.vfs_stress_strain[idx][:,:,2] + cons[3] = self.load_im[idx][:,:,0] + cons[4] = self.load_im[idx][:,:,1] + return np.expand_dims(self.topologies[idx], 0) * 2 - 1, cons def load_data_topodiff( - topologies, constraints, stress, strain, load_img, batch_size, deterministic=False + topologies, vfs_stress_strain, load_im, batch_size, deterministic=False ): dataset = DiffusionDataset_topodiff( - topologies, stress, strain, load_img, constraints + topologies, vfs_stress_strain, load_im ) if deterministic: @@ -113,4 +108,63 @@ def load_data_topodiff( dataset, batch_size=batch_size, shuffle=True, num_workers=1, drop_last=True ) while True: - yield from loader \ No newline at end of file + yield from loader + +def load_data(root, prefix, file_format, num_file_start=0,num_file_end=30000): + """ + root: path to the folder of training data + prefix: file prefix to the ground truth topology, boundary condition and stress/strain + file_format: .npy for the conditions; .png for the ground truth topologies + """ + data_array = [] + + for i in range(num_file_start, num_file_end): + file= f'{root}{prefix}{i}{file_format}' + if file_format == '.npy': + data_array.append(np.load(file)) + elif file_format == '.png': + data_array.append(np.array(Image.open(file))/255) + else: + raise NotImplementedError + + return np.array(data_array).astype(np.float64) + +def load_data_regressor(root): + + file_list = os.listdir(root) + idx_list = [] + for file in file_list: + if file.startswith('gt_topo_'): + idx = int(file.split('.')[0][8:]) + idx_list.append(idx) + idx_list.sort() + + topology_array, load_array, pf_array = [], [], [] + for i in idx_list: + + topology_array.append(np.array(Image.open(root + "gt_topo_" + str(i) + '.png'))/255) + load_array.append(np.load(root + "cons_load_array_" + str(i) + '.npy')) + pf_array.append(np.load(root + "cons_pf_array_" + str(i) + '.npy')) + + labels = np.load(root + 'deflections_scaled_diff.npy') + return np.array(topology_array).astype(np.float64), np.array(load_array).astype(np.float64), np.array(pf_array).astype(np.float64), labels[idx_list] + + +def load_data_classifier(root): + """ + root: path to the folder of training data + prefix: file prefix to the ground truth topology, boundary condition and stress/strain + file_format: .npy for the conditions; .png for the ground truth topologies + """ + file_list= os.listdir(root) + labels = np.load(root + 'labels.npy') + image_list = [] + label_list = [] + for file in file_list: + if file.startswith('img_'): + idx = int(file.split('.')[0][4:]) + image = Image.open(root + file) + image_list.append(np.array(image)/255) + label_list.append(labels[idx]) + + return np.array(image_list).astype(np.float64), np.array(label_list).astype(np.float64) \ No newline at end of file From 9caf7cd586d7ca1fa325a889427e690680ff9d56 Mon Sep 17 00:00:00 2001 From: FaezTurboGTS2000 Date: Fri, 1 Nov 2024 00:14:41 +0000 Subject: [PATCH 21/23] Topodiff merge --- examples/generative/topodiff/README.md | 23 +++++++++++++++---- examples/generative/topodiff/conf/config.yaml | 2 +- .../generative/topodiff/train_classifier.py | 2 +- .../generative/topodiff/train_regressor.py | 7 +++--- 4 files changed, 25 insertions(+), 9 deletions(-) diff --git a/examples/generative/topodiff/README.md b/examples/generative/topodiff/README.md index 310699aac2..3f2695a798 100644 --- a/examples/generative/topodiff/README.md +++ b/examples/generative/topodiff/README.md @@ -8,7 +8,11 @@ We propose Topodiff, a conditional diffusion-model-based architecture to perform

## Dataset -Link to the dataset: [here](https://www.dropbox.com/home/decode_lab/Datasets/Public%20Documents/Topodiff_dataset) +- Link to the complete dataset: [here](https://www.dropbox.com/home/decode_lab/Datasets/Public%20Documents/Topodiff_dataset) +-Link to the dataset for diffusion model training: [here](https://www.dropbox.com/scl/fi/9jy96a0lyf39wdwc27se7/dataset_1_diff.zip?rlkey=zz0ijw8e5h0hf0fb7qpnbwrgu&st=t7nuh1w7&dl=0) +Link to the dataset for regression model training: [here](https://www.dropbox.com/scl/fi/486kmqbghzxuxqewjm9b4/dataset_2_reg.zip?rlkey=gw8yu1lv40tqk192py7wsl9o9&st=ao9yc1rw&dl=0) +Link to the dataset for classifier model training: [here](https://www.dropbox.com/scl/fi/486kmqbghzxuxqewjm9b4/dataset_2_reg.zip?rlkey=gw8yu1lv40tqk192py7wsl9o9&st=ao9yc1rw&dl=0) + Download the dataset and set the **path_data** in [the config yaml file](conf/config.yaml) @@ -30,17 +34,28 @@ hydra: path_data: path to the Topodiff dataset downloaded -epochs: 200000 +epochs: 100000 batch_size: 128 lr: 1e-4 -classifier_iterations: 30000 -regressor_iterations: 30000 +classifier_iterations: 20000 +regressor_iterations: 20000 diffusion_steps: 1000 generation_path: ./ + model_path_diffusion: path to the pt file of the diffusion model model_path_classifier: path to the pt file of the classifier for floating material + + +path_training_data_diffusion: path to the /dataset_1_diff/training_data/ + +path_data_regressor_training: path to the /dataset_2_reg/training_data/ +path_data_regressor_validation: path to the /dataset_2_reg/validation_data/ + +path_data_classifier_training: path to the /dataset_3_class/training_data/ +path_data_classifier_validation: path to the /dataset_3_class/validation_data/ + ``` Run the following command to train the diffusion model, classifier for floating material and regressor for compliance: diff --git a/examples/generative/topodiff/conf/config.yaml b/examples/generative/topodiff/conf/config.yaml index 5af2460be3..4865bd1d04 100644 --- a/examples/generative/topodiff/conf/config.yaml +++ b/examples/generative/topodiff/conf/config.yaml @@ -21,7 +21,7 @@ hydra: dir: ./outputs/ -epochs: 500000 +epochs: 100000 batch_size: 64 lr: 1e-4 diff --git a/examples/generative/topodiff/train_classifier.py b/examples/generative/topodiff/train_classifier.py index 27ec663e03..01b037a8d7 100644 --- a/examples/generative/topodiff/train_classifier.py +++ b/examples/generative/topodiff/train_classifier.py @@ -53,7 +53,7 @@ def main(cfg: DictConfig) -> None: optimizer = AdamW(classifier.parameters(), lr=lr) scheduler = LinearLR(optimizer, start_factor=1, end_factor=0.001, total_iters=cfg.classifier_iterations) - for i in range(cfg.classifier_iterations): + for i in range(cfg.classifier_iterations+1): # get random batch from training data idx = np.random.choice(len(train_img), batch_size, replace=False) diff --git a/examples/generative/topodiff/train_regressor.py b/examples/generative/topodiff/train_regressor.py index 375ff4ea2b..023be6cc24 100644 --- a/examples/generative/topodiff/train_regressor.py +++ b/examples/generative/topodiff/train_regressor.py @@ -28,6 +28,7 @@ def main(cfg: DictConfig) -> None: topologies, load_imgs, vfs_stress_strain, labels = load_data_regressor(cfg.path_data_regressor_training) topologies = topologies*2 - 1 # Normalize the range of image to be [-1, 1] + print(topologies.min(), topologies.max()) """ topologies = np.load(cfg.path_data + "Compliance/Training/topologies.npy").astype(np.float64) constraints = np.load(cfg.path_data + "Compliance/Training/constraints.npy", allow_pickle=True) @@ -57,7 +58,7 @@ def main(cfg: DictConfig) -> None: scheduler = LinearLR(optimizer, start_factor=1, end_factor=0.001, total_iters=cfg.regressor_iterations) loss_fn = nn.MSELoss() - for i in range(cfg.regressor_iterations): + for i in range(cfg.regressor_iterations+1): # get random batch from training data idx = np.random.choice(len(topologies), batch_size, replace=False) @@ -84,8 +85,8 @@ def main(cfg: DictConfig) -> None: if i % 100 == 0: print("epoch: %d, loss: %.5f" % (i, loss.item())) - if i % 10000 == 0: - torch.save(regressor.state_dict(), cfg.model_path + "regressor.pt") + + torch.save(regressor.state_dict(), cfg.model_path + "regressor.pt") print("job done!") From f69cc1f760126d68917e652996fe8c19241c2bb2 Mon Sep 17 00:00:00 2001 From: FaezTurboGTS2000 Date: Fri, 1 Nov 2024 00:15:18 +0000 Subject: [PATCH 22/23] Topodiff merge --- examples/generative/topodiff/README.md | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/examples/generative/topodiff/README.md b/examples/generative/topodiff/README.md index 3f2695a798..13d5e705cc 100644 --- a/examples/generative/topodiff/README.md +++ b/examples/generative/topodiff/README.md @@ -9,9 +9,9 @@ We propose Topodiff, a conditional diffusion-model-based architecture to perform ## Dataset - Link to the complete dataset: [here](https://www.dropbox.com/home/decode_lab/Datasets/Public%20Documents/Topodiff_dataset) --Link to the dataset for diffusion model training: [here](https://www.dropbox.com/scl/fi/9jy96a0lyf39wdwc27se7/dataset_1_diff.zip?rlkey=zz0ijw8e5h0hf0fb7qpnbwrgu&st=t7nuh1w7&dl=0) -Link to the dataset for regression model training: [here](https://www.dropbox.com/scl/fi/486kmqbghzxuxqewjm9b4/dataset_2_reg.zip?rlkey=gw8yu1lv40tqk192py7wsl9o9&st=ao9yc1rw&dl=0) -Link to the dataset for classifier model training: [here](https://www.dropbox.com/scl/fi/486kmqbghzxuxqewjm9b4/dataset_2_reg.zip?rlkey=gw8yu1lv40tqk192py7wsl9o9&st=ao9yc1rw&dl=0) +- Link to the dataset for diffusion model training: [here](https://www.dropbox.com/scl/fi/9jy96a0lyf39wdwc27se7/dataset_1_diff.zip?rlkey=zz0ijw8e5h0hf0fb7qpnbwrgu&st=t7nuh1w7&dl=0) +- Link to the dataset for regression model training: [here](https://www.dropbox.com/scl/fi/486kmqbghzxuxqewjm9b4/dataset_2_reg.zip?rlkey=gw8yu1lv40tqk192py7wsl9o9&st=ao9yc1rw&dl=0) +- Link to the dataset for classifier model training: [here](https://www.dropbox.com/scl/fi/486kmqbghzxuxqewjm9b4/dataset_2_reg.zip?rlkey=gw8yu1lv40tqk192py7wsl9o9&st=ao9yc1rw&dl=0) Download the dataset and set the **path_data** in [the config yaml file](conf/config.yaml) From 691476b5213f1870cc4ba21d4ff881fb7136e7d4 Mon Sep 17 00:00:00 2001 From: FaezTurboGTS2000 Date: Fri, 1 Nov 2024 20:07:40 +0000 Subject: [PATCH 23/23] Topodiff merge --- examples/generative/topodiff/train.py | 10 +--------- examples/generative/topodiff/train_classifier.py | 14 ++++---------- examples/generative/topodiff/train_regressor.py | 3 +-- modulus/models/topodiff/topodiff.py | 4 ++-- 4 files changed, 8 insertions(+), 23 deletions(-) diff --git a/examples/generative/topodiff/train.py b/examples/generative/topodiff/train.py index f469015f6d..100168907b 100644 --- a/examples/generative/topodiff/train.py +++ b/examples/generative/topodiff/train.py @@ -22,14 +22,7 @@ def main(cfg: DictConfig) -> None: logger = PythonLogger("main") # General Python Logger logger.log("Job start!") - """ - topologies = np.load(cfg.path_data + "topologies.npy").astype(np.float64) - constraints = np.load(cfg.path_data + "constraints.npy", allow_pickle=True) - stress = np.load(cfg.path_data + "vonmises.npy", allow_pickle=True) - strain = np.load(cfg.path_data + "strain_energy.npy", allow_pickle=True) - load_imgs = np.load(cfg.path_data + "load_ims.npy") - labels = np.load(cfg.path_data+ "Floating/training_labels.npy").astype(np.float64) - """ + device = torch.device('cuda:0') model = TopoDiff(64, 6, 1, model_channels=128, attn_resolutions=[16,8]).to(device) diffusion = Diffusion(n_steps=1000,device=device) @@ -43,7 +36,6 @@ def main(cfg: DictConfig) -> None: data = load_data_topodiff( topologies, vfs_stress_strain, load_imgs, batch_size= batch_size,deterministic=False ) - print("the min and max of topologies: ", topologies.min(), topologies.max()) lr = cfg.lr optimizer = AdamW(model.parameters(), lr=lr) diff --git a/examples/generative/topodiff/train_classifier.py b/examples/generative/topodiff/train_classifier.py index 01b037a8d7..66eaf6b8b7 100644 --- a/examples/generative/topodiff/train_classifier.py +++ b/examples/generative/topodiff/train_classifier.py @@ -27,23 +27,16 @@ def main(cfg: DictConfig) -> None: logger = PythonLogger("main") # General Python Logger logger.log("Start running") - #train_img = np.load(cfg.path_data + "Floating/training.npy").astype(np.float64) - #train_labels = np.load(cfg.path_data + "Floating/training_labels.npy").astype(np.float64) - - #valid_img = np.load(cfg.path_data + "Floating/validation.npy").astype(np.float64) - #valid_labels = np.load(cfg.path_data + "Floating/validation_labels.npy").astype(np.float64) - train_img, train_labels = load_data_classifier(cfg.path_data_classifier_training) valid_img, valid_labels = load_data_classifier(cfg.path_data_classifier_validation) train_img = 2 * train_img - 1 valid_img = 2 * valid_img - 1 - print(train_img.min(), train_img.max()) - print(valid_img.min(), valid_img.max()) + device = torch.device('cuda:1') classifier = UNetEncoder(in_channels = 1, out_channels=2).to(device) - classifier.load_state_dict(torch.load('/home/turbo/Qian/modulus/modulus/outputs/classifier.pt')) + diffusion = Diffusion(n_steps=cfg.diffusion_steps,device=device) batch_size = cfg.batch_size @@ -89,7 +82,8 @@ def main(cfg: DictConfig) -> None: accuracy = correct_predictions / batch_size print("epoch: %d, loss: %.5f, validation accuracy: %.3f" % (i, loss.item(), accuracy)) - + #if i % 10000 == 0: + # torch.save(classifier.state_dict(), cfg.model_path + "classifier_" +str(i) + ".pt") torch.save(classifier.state_dict(), cfg.model_path + "classifier.pt") print("job done!") diff --git a/examples/generative/topodiff/train_regressor.py b/examples/generative/topodiff/train_regressor.py index 023be6cc24..0b7563c9ab 100644 --- a/examples/generative/topodiff/train_regressor.py +++ b/examples/generative/topodiff/train_regressor.py @@ -28,7 +28,6 @@ def main(cfg: DictConfig) -> None: topologies, load_imgs, vfs_stress_strain, labels = load_data_regressor(cfg.path_data_regressor_training) topologies = topologies*2 - 1 # Normalize the range of image to be [-1, 1] - print(topologies.min(), topologies.max()) """ topologies = np.load(cfg.path_data + "Compliance/Training/topologies.npy").astype(np.float64) constraints = np.load(cfg.path_data + "Compliance/Training/constraints.npy", allow_pickle=True) @@ -63,7 +62,7 @@ def main(cfg: DictConfig) -> None: # get random batch from training data idx = np.random.choice(len(topologies), batch_size, replace=False) batch = torch.tensor(topologies[idx]).float().unsqueeze(1).to(device)*2-1 # 4 x 1 x 64 x 64 - batch_pf = torch.tensor(vfs_stress_strain[idx]).float().permute(0,3,1,2).to(device)*2-1 + batch_pf = torch.tensor(vfs_stress_strain[idx]).float().permute(0,3,1,2).to(device) batch_load = torch.tensor(load_imgs[idx]).float().permute(0,3,1,2).to(device) batch_labels = torch.tensor(labels[idx]).float().to(device).unsqueeze(1) diff --git a/modulus/models/topodiff/topodiff.py b/modulus/models/topodiff/topodiff.py index a4d28eddc3..6f350ece48 100644 --- a/modulus/models/topodiff/topodiff.py +++ b/modulus/models/topodiff/topodiff.py @@ -258,9 +258,9 @@ def __init__( out_channels: int, model_channels: int = 128, num_res_blocks: int = 4, - channel_mult: tuple = (1, 2, 4, 8), + channel_mult: tuple = (1, 1, 1, 1), channel_mult_emb: int = 4, - attention_resolutions: tuple = (2, 4, 8), + attention_resolutions: tuple = (16, 8), dropout=0, output_prob=False):

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