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postprocess.py
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"""
Postprocess trained model (no DDP)
"""
from __future__ import absolute_import, division, print_function, annotations
import os
import socket
import logging
import copy
from typing import Optional, Union, Callable
import numpy as np
import matplotlib.pyplot as plt
plt.rcParams.update({'font.size': 22})
import time
import torch
import torch.utils.data
import torch.distributions as tdist
import torch.nn as nn
import torch.optim as optim
import torch.nn.functional as F
from torchvision import datasets, transforms
from omegaconf import DictConfig
Tensor = torch.Tensor
# PyTorch Geometric
import torch_geometric
# Models
#import models.gnn as gnn
import models.gnn_topk_relu as gnn
# Data preparation
import dataprep.unstructured_mnist as umnist
import dataprep.backward_facing_step as bfs
def scalar2openfoam(image_vec, filename, objectname, time_value):
time_write = time.time()
with open(filename, 'w') as f:
# Openfoam header:
f.write('/*--------------------------------*- C++ -*----------------------------------*\\\n')
f.write('| ========= | |\n')
f.write('| \\\ / F ield | OpenFOAM: The Open Source CFD Toolbox |\n')
f.write('| \\\ / O peration | Version: v2006 |\n')
f.write('| \\\ / A nd | Web: www.OpenFOAM.org |\n')
f.write('| \\\/ M anipulation | |\n')
f.write('\\*---------------------------------------------------------------------------*/\n')
# FoamFile part:
f.write('FoamFile\n')
f.write('{\n')
f.write('\tversion\t\t2.0;\n')
f.write('\tformat\t\tascii;\n')
f.write('\tclass\t\tvolScalarField;\n')
f.write('\tlocation\t"%g";\n' %(time_value))
f.write('\tobject\t\t%s;\n' %(objectname))
f.write('}\n')
f.write('// * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * //\n')
f.write('\n')
# internalField
f.write('dimensions\t\t[0 0 0 0 0 0 0];\n\n')
f.write('internalField\tnonuniform List<scalar>\n')
f.write('%s\n' %(len(image_vec)))
f.write('(\n')
for val in image_vec:
f.write('%s\n' %(val))
f.write(')\n')
f.write(';\n\n')
# boundaryField -- do cyclic / empty for now
f.write('boundaryField\n')
f.write('{\n')
# left:
f.write('\tinlet\n')
f.write('\t{\n')
f.write('\t\ttype\t\t\tzeroGradient;\n')
f.write('\t}\n')
# right:
f.write('\toutlet\n')
f.write('\t{\n')
f.write('\t\ttype\t\t\tfixedValue;\n')
f.write('\t\tvalue\t\t\tuniform 0;\n')
f.write('\t}\n')
# top:
f.write('\t\"(lowerWallStartup|upperWallStartup)\"\n')
f.write('\t{\n')
f.write('\t\ttype\t\t\tsymmetryPlane;\n')
f.write('\t}\n')
# bottom:
f.write('\t\"(upperWall|lowerWall)\"\n')
f.write('\t{\n')
f.write('\t\ttype\t\t\tzeroGradient;\n')
f.write('\t}\n')
# front:
f.write('\t\"(front|back)\"\n')
f.write('\t{\n')
f.write('\t\ttype\t\t\tempty;\n')
f.write('\t}\n')
#oldInternalFaces
f.write('\t\"oldInternalFaces\"\n')
f.write('\t{\n')
f.write('\t\ttype\t\t\tinternal;\n')
f.write('\t\tvalue\t\t\tuniform 0;\n')
f.write('\t}\n')
f.write('}\n\n')
f.write('// ************************************************************************* //')
time_write = time.time() - time_write
print('%s: \t\t\t%.5e s' %(filename, time_write))
seed = 42
torch.set_grad_enabled(False)
dataset_dir = './datasets/BACKWARD_FACING_STEP/'
# ~~~~ For making pygeom dataset from VTK
# # Get statistics using combined dataset:
# path_to_vtk = 'datasets/BACKWARD_FACING_STEP/cropped/Backward_Facing_Step_Cropped_Re_26214_29307_39076_45589.vtk'
# data_mean, data_std = bfs.get_data_statistics(
# path_to_vtk,
# multiple_cases = True)
# Get statistics for big dataset:
stats = np.load('./datasets/BACKWARD_FACING_STEP/full/20_cases/stats.npz')
data_mean = stats['mean']
data_std = stats['std']
seed_list = None
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
# Postprocess training losses: ORIGINAL
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
if 1 == 0:
print('Postprocess training losses (original)')
# # Comparing baselines:
# a = torch.load('saved_models/big_data/dt_gnn_1em4/NO_RADIUS_LR_1em5_topk_unet_rollout_1_seed_82_down_topk_2_up_topk_factor_4_hc_128_down_enc_2_2_2_up_enc_2_2_down_dec_2_2_2_up_dec_2_2_param_sharing_0.tar')
# a_label = 'Baseline, Big data, With Noise'
# b = torch.load('saved_models/big_data/dt_gnn_1em4/NO_NOISE_NO_RADIUS_LR_1em5_topk_unet_rollout_1_seed_82_down_topk_2_up_topk_factor_4_hc_128_down_enc_2_2_2_up_enc_2_2_down_dec_2_2_2_up_dec_2_2_param_sharing_0.tar')
# b_label = 'Baseline, Big data, No Noise'
# c = torch.load('saved_models/small_data/NO_RADIUS_LR_1em5_topk_unet_rollout_1_seed_82_down_topk_2_up_topk_factor_4_hc_128_down_enc_2_2_2_up_enc_2_2_down_dec_2_2_2_up_dec_2_2_param_sharing_0.tar')
# c_label = 'Baseline, Small Data, With Noise'
# Comparing fine-tuning
bl = torch.load('saved_models/big_data/dt_gnn_1em4/NO_RADIUS_LR_1em5_topk_unet_rollout_1_seed_82_down_topk_2_up_topk_factor_4_hc_128_down_enc_2_2_2_up_enc_2_2_down_dec_2_2_2_up_dec_2_2_param_sharing_0.tar')
bl_label = 'Baseline'
desc = 'no_budget_reg'
ft_2 = torch.load('saved_models/big_data/dt_gnn_1em4/%s/NO_RADIUS_LR_1em5_pretrained_topk_unet_rollout_1_seed_65_down_topk_1_1_up_topk_1_factor_2_hc_128_down_enc_2_2_2_up_enc_2_2_down_dec_2_2_2_up_dec_2_2_param_sharing_0.tar' %(desc))
ft_2_label = 'Finetuned, RF=2'
ft_4 = torch.load('saved_models/big_data/dt_gnn_1em4/%s/NO_RADIUS_LR_1em5_pretrained_topk_unet_rollout_1_seed_65_down_topk_1_1_up_topk_1_factor_4_hc_128_down_enc_2_2_2_up_enc_2_2_down_dec_2_2_2_up_dec_2_2_param_sharing_0.tar' %(desc))
ft_4_label = 'Finetuned, RF=4'
ft_8 = torch.load('saved_models/big_data/dt_gnn_1em4/%s/NO_RADIUS_LR_1em5_pretrained_topk_unet_rollout_1_seed_65_down_topk_1_1_up_topk_1_factor_8_hc_128_down_enc_2_2_2_up_enc_2_2_down_dec_2_2_2_up_dec_2_2_param_sharing_0.tar' %(desc))
ft_8_label = 'Finetuned, RF=8'
ft_16 = torch.load('saved_models/big_data/dt_gnn_1em4/%s/NO_RADIUS_LR_1em5_pretrained_topk_unet_rollout_1_seed_65_down_topk_1_1_up_topk_1_factor_16_hc_128_down_enc_2_2_2_up_enc_2_2_down_dec_2_2_2_up_dec_2_2_param_sharing_0.tar' %(desc))
ft_16_label = 'Finetuned, RF=16'
ft_16_topkrelu = torch.load('saved_models/big_data/dt_gnn_1em4/%s/TOPK_RELU_NO_RADIUS_LR_1em5_pretrained_topk_unet_rollout_1_seed_65_down_topk_1_1_up_topk_1_factor_16_hc_128_down_enc_2_2_2_up_enc_2_2_down_dec_2_2_2_up_dec_2_2_param_sharing_0.tar' %(desc))
ft_16_topkrelu_label = 'Finetuned, RF=16, topkrelu'
desc = 'budget_reg_lam_0.001'
ft_2_br = torch.load('saved_models/big_data/dt_gnn_1em4/%s/NO_RADIUS_LR_1em5_BUDGET_REG_pretrained_topk_unet_rollout_1_seed_65_down_topk_1_1_up_topk_1_factor_2_hc_128_down_enc_2_2_2_up_enc_2_2_down_dec_2_2_2_up_dec_2_2_param_sharing_0.tar' %(desc))
ft_2_br_label = 'Finetuned, RF=2'
ft_4_br = torch.load('saved_models/big_data/dt_gnn_1em4/%s/NO_RADIUS_LR_1em5_BUDGET_REG_pretrained_topk_unet_rollout_1_seed_65_down_topk_1_1_up_topk_1_factor_4_hc_128_down_enc_2_2_2_up_enc_2_2_down_dec_2_2_2_up_dec_2_2_param_sharing_0.tar' %(desc))
ft_4_br_label = 'Finetuned, RF=4'
ft_8_br = torch.load('saved_models/big_data/dt_gnn_1em4/%s/NO_RADIUS_LR_1em5_BUDGET_REG_pretrained_topk_unet_rollout_1_seed_65_down_topk_1_1_up_topk_1_factor_8_hc_128_down_enc_2_2_2_up_enc_2_2_down_dec_2_2_2_up_dec_2_2_param_sharing_0.tar' %(desc))
ft_8_br_label = 'Finetuned, RF=8'
ft_16_br = torch.load('saved_models/big_data/dt_gnn_1em4/%s/NO_RADIUS_LR_1em5_BUDGET_REG_pretrained_topk_unet_rollout_1_seed_65_down_topk_1_1_up_topk_1_factor_16_hc_128_down_enc_2_2_2_up_enc_2_2_down_dec_2_2_2_up_dec_2_2_param_sharing_0.tar' %(desc))
ft_16_br_label = 'Finetuned, RF=16'
# Looking at the lambda test
# LAM = -0.0002459254785
# Combined loss plot
baseline_loss = np.mean(bl['loss_hist_test'][-10:])
# No budget reg
combined = [ft_2, ft_4, ft_8, ft_16, ft_16_topkrelu]
labels = [ft_2_label, ft_4_label, ft_8_label, ft_16_label, ft_16_topkrelu_label]
fig, ax = plt.subplots(figsize=(8,6))
ax.axhline(y=baseline_loss, color='black', linestyle='--', lw=2)
for i in range(len(combined)):
ax.plot(combined[i]['loss_hist_test'][1:], lw=2, label=labels[i])
#ax.set_yscale('log')
#ax.set_xscale('log')
ax.set_ylabel('MSE')
ax.set_xlabel('Epochs')
ax.legend(fancybox=False, edgecolor='black', framealpha=1)
ax.set_title('Validation Loss -- Lam = 0')
plt.show(block=False)
asdf
# With budget reg
combined = [ft_2_br, ft_4_br, ft_8_br, ft_16_br]
labels = [ft_2_br_label, ft_4_br_label, ft_8_br_label, ft_16_br_label]
fig, ax = plt.subplots(figsize=(8,6))
ax.axhline(y=baseline_loss, color='black', linestyle='--', lw=2)
for i in range(len(combined)):
ax.plot(combined[i]['loss_hist_test_comp2'][1:], lw=2, label=labels[i])
#ax.set_yscale('log')
#ax.set_xscale('log')
ax.set_ylabel('MSE')
ax.set_xlabel('Epochs')
#ax.legend(fancybox=False, edgecolor='black', framealpha=1)
ax.set_title('Validation Loss -- Lam = 0.001')
plt.show(block=False)
# Lambda test
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
# Overwrite some model names:
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
if 1 == 0:
print('Overwrite some model names...')
seed_list = [105, 122, 132, 142, 152, 162, 172, 182, 192, 202, 212, 222,
42, 65, 82]
for seed in seed_list:
b = torch.load('saved_models/pretrained_topk_unet_rollout_1_seed_%d_down_topk_1_1_up_topk_1_factor_4_hc_128_down_enc_2_2_2_up_enc_2_2_down_dec_2_2_2_up_dec_2_2_param_sharing_0.tar' %(seed))
model_name = b['input_dict']['name']
if 'seed' not in model_name:
print('\tcorrecting name for model of seed ', seed)
rollout_steps = 1
model_name_new = 'pretrained_topk_unet_rollout_%d_seed_%d' %(rollout_steps, seed)
b['input_dict']['name'] = model_name_new
torch.save(b, 'saved_models/pretrained_topk_unet_rollout_1_seed_%d_down_topk_1_1_up_topk_1_factor_4_hc_128_down_enc_2_2_2_up_enc_2_2_down_dec_2_2_2_up_dec_2_2_param_sharing_0.tar' %(seed))
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
# Postprocess training losses: FOCUS ON EFFECT OF SEEDING
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
if 1 == 0:
print('Postprocess training losses: focus on effect of seeding.')
# baseline:
a = torch.load('saved_models/NO_RADIUS_LR_1em5_topk_unet_rollout_1_seed_82_down_topk_2_up_topk_factor_4_hc_128_down_enc_2_2_2_up_enc_2_2_down_dec_2_2_2_up_dec_2_2_param_sharing_0.tar')
a_label = 'Baseline (rollout = 1)'
seed_list = torch.tensor([42, 65, 82, 105, 122, 132, 142, 152, 162, 172])
topk_models_converged_loss = []
topk_models = []
for seed in seed_list:
b = torch.load('saved_models/NO_RADIUS_LR_1em5_pretrained_topk_unet_rollout_1_seed_%d_down_topk_1_1_up_topk_1_factor_4_hc_128_down_enc_2_2_2_up_enc_2_2_down_dec_2_2_2_up_dec_2_2_param_sharing_0.tar' %(seed))
#b = torch.load('saved_models/NO_RADIUS_LR_1em5_BUDGET_REG_pretrained_topk_unet_rollout_1_seed_%d_down_topk_1_1_up_topk_1_factor_4_hc_128_down_enc_2_2_2_up_enc_2_2_down_dec_2_2_2_up_dec_2_2_param_sharing_0.tar' %(seed))
topk_models.append(b)
topk_models_converged_loss.append(b['loss_hist_train'][-1])
# Re-order based on converged loss
_, sort_idx = torch.sort(torch.tensor(topk_models_converged_loss))
seed_list = seed_list[sort_idx]
# Re-read:
topk_models_converged_loss = []
topk_models = []
for seed in seed_list:
b = torch.load('saved_models/NO_RADIUS_LR_1em5_pretrained_topk_unet_rollout_1_seed_%d_down_topk_1_1_up_topk_1_factor_4_hc_128_down_enc_2_2_2_up_enc_2_2_down_dec_2_2_2_up_dec_2_2_param_sharing_0.tar' %(seed))
#b = torch.load('saved_models/NO_RADIUS_LR_1em5_BUDGET_REG_pretrained_topk_unet_rollout_1_seed_%d_down_topk_1_1_up_topk_1_factor_4_hc_128_down_enc_2_2_2_up_enc_2_2_down_dec_2_2_2_up_dec_2_2_param_sharing_0.tar' %(seed))
topk_models.append(b)
topk_models_converged_loss.append(b['loss_hist_train'][-1])
# Combined loss plot
baseline_loss = np.mean(a['loss_hist_train'][-10:])
fig, ax = plt.subplots(figsize=(10,8))
ax.axhline(y=baseline_loss, color='black', linestyle='--', lw=2)
for i in range(len(seed_list)):
ax.plot(topk_models[i]['loss_hist_train'][:], lw=2, zorder=-1)
#ax.set_yscale('log')
#ax.set_xscale('log')
ax.set_ylabel('MSE')
ax.set_xlabel('Epochs')
plt.show(block=False)
# Components plot
fig, ax = plt.subplots(1,2,figsize=(10,8))
#ax.axhline(y=baseline_loss, color='black', linestyle='--', lw=2)
i = 0
ax[0].plot(topk_models[i]['loss_hist_train_comp1'][:], lw=2, zorder=-1, label='MSE')
ax[0].set_ylabel('MSE')
ax[0].set_xlabel('Epochs')
ax[0].set_title('MSE Term')
ax[1].plot(topk_models[i]['loss_hist_train_comp2'][:], lw=2, zorder=-1, label='Budget Reg')
ax[1].set_ylabel('lam * (1/B)')
ax[1].set_xlabel('Epochs')
ax[1].set_title('Budget Reg. Term')
plt.show(block=False)
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
# Baseline error budget: what percent of baseline error is in masked region?
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
if 1 == 0:
if torch.cuda.is_available():
device = 'cuda:0'
else:
device = 'cpu'
# Read data:
if 1 == 0:
modelname_list = []
#modelname_list = ['topk_unet_rollout_1_down_topk_2_up_topk_factor_4_hc_128_down_enc_2_2_2_up_enc_2_2_down_dec_2_2_2_up_dec_2_2_param_sharing_0']
mse_mask_list = []
mse_full_list = []
for seed in seed_list:
#mse_mask = np.load('outputs/postproc/budget_without_reg/mse_mask_NO_RADIUS_LR_1em5_pretrained_topk_unet_rollout_1_seed_%d_down_topk_1_1_up_topk_1_factor_4_hc_128_down_enc_2_2_2_up_enc_2_2_down_dec_2_2_2_up_dec_2_2_param_sharing_0.npy' %(seed))[:, 2:, :]
mse_mask = np.load('outputs/postproc/budget_with_reg/mse_mask_NO_RADIUS_LR_1em5_BUDGET_REG_pretrained_topk_unet_rollout_1_seed_%d_down_topk_1_1_up_topk_1_factor_4_hc_128_down_enc_2_2_2_up_enc_2_2_down_dec_2_2_2_up_dec_2_2_param_sharing_0.npy' %(seed))[:, 2:, :]
mse_mask_list.append(mse_mask)
#mse_full = np.load('outputs/postproc/budget_without_reg/mse_full_NO_RADIUS_LR_1em5_pretrained_topk_unet_rollout_1_seed_%d_down_topk_1_1_up_topk_1_factor_4_hc_128_down_enc_2_2_2_up_enc_2_2_down_dec_2_2_2_up_dec_2_2_param_sharing_0.npy' %(seed))[:, 2:, :]
mse_full = np.load('outputs/postproc/budget_with_reg/mse_full_NO_RADIUS_LR_1em5_BUDGET_REG_pretrained_topk_unet_rollout_1_seed_%d_down_topk_1_1_up_topk_1_factor_4_hc_128_down_enc_2_2_2_up_enc_2_2_down_dec_2_2_2_up_dec_2_2_param_sharing_0.npy' %(seed))[:, 2:, :]
mse_full_list.append(mse_full)
rollout_id = 0
# Percentage:
fig, ax = plt.subplots(3,5, figsize=(16,8), sharex=True, sharey=False)
for r in range(3):
for c in range(5):
seed_id = r*5 + c
if seed_id < len(mse_full_list):
for comp in range(2):
mse_full = mse_full_list[seed_id][rollout_id,:,comp]
mse_mask = mse_mask_list[seed_id][rollout_id,:,comp]
mse_not_mask = mse_full - mse_mask
percent_mask = (mse_mask/mse_full)*100
percent_not_mask = (mse_not_mask/mse_full)*100
ax[r,c].plot(percent_mask)
#ax[r,c].plot(mse_mask)
#ax[r,c].plot(percent_not_mask, color='black')
ax[r,c].set_title('Seed = %d' %(seed_list[seed_id]))
ax[r,c].set_ylim([0,100])
# ax[r,c].set_ylim([1e-5, 1e-2])
# ax[r,c].set_yscale('log')
#ax.set_ylabel('MSE Budget [%]')
#ax.set_xlabel('Time')
plt.show(block=False)
# Just plot one
for seed_id in range(len(seed_list)):
fig, ax = plt.subplots()
for comp in range(2):
mse_full = mse_full_list[seed_id][rollout_id,:,comp]
mse_mask = mse_mask_list[seed_id][rollout_id,:,comp]
mse_not_mask = mse_full - mse_mask
percent_mask = (mse_mask/mse_full)*100
percent_not_mask = (mse_not_mask/mse_full)*100
ax.plot(percent_mask)
ax.set_title('Seed = %d' %(seed_list[seed_id]))
ax.set_ylim([0,100])
ax.set_ylabel('MSE Budget [%]')
ax.set_xlabel('Time Step')
plt.show(block=False)
# Write data, with focus on effect of seeding:
if 1 == 0:
print('Writing budget data, with focus on effect of seeding...')
seed_list = [42, 65, 82, 105, 122, 132, 142, 152, 162, 172]
for seed in seed_list:
print('~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~')
print('SEED %d' %(seed))
print('~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~')
#modelpath_topk = 'saved_models/NO_RADIUS_LR_1em5_pretrained_topk_unet_rollout_1_seed_%d_down_topk_1_1_up_topk_1_factor_4_hc_128_down_enc_2_2_2_up_enc_2_2_down_dec_2_2_2_up_dec_2_2_param_sharing_0.tar' %(seed)
modelpath_topk = 'saved_models/NO_RADIUS_LR_1em5_BUDGET_REG_pretrained_topk_unet_rollout_1_seed_%d_down_topk_1_1_up_topk_1_factor_4_hc_128_down_enc_2_2_2_up_enc_2_2_down_dec_2_2_2_up_dec_2_2_param_sharing_0.tar' %(seed)
p = torch.load(modelpath_topk)
input_dict = p['input_dict']
model_topk = gnn.GNN_TopK_NoReduction(
in_channels_node = input_dict['in_channels_node'],
in_channels_edge = input_dict['in_channels_edge'],
hidden_channels = input_dict['hidden_channels'],
out_channels = input_dict['out_channels'],
n_mlp_encode = input_dict['n_mlp_encode'],
n_mlp_mp = input_dict['n_mlp_mp'],
n_mp_down_topk = input_dict['n_mp_down_topk'],
n_mp_up_topk = input_dict['n_mp_up_topk'],
pool_ratios = input_dict['pool_ratios'],
n_mp_down_enc = input_dict['n_mp_down_enc'],
n_mp_up_enc = input_dict['n_mp_up_enc'],
n_mp_down_dec = input_dict['n_mp_down_dec'],
n_mp_up_dec = input_dict['n_mp_up_dec'],
lengthscales_enc = input_dict['lengthscales_enc'],
lengthscales_dec = input_dict['lengthscales_dec'],
bounding_box = input_dict['bounding_box'],
interpolation_mode = input_dict['interp'],
act = input_dict['act'],
param_sharing = input_dict['param_sharing'],
filter_lengthscale = input_dict['filter_lengthscale'],
name = input_dict['name'])
model_topk.load_state_dict(p['state_dict'])
model_topk.to(device)
model_topk.eval()
# ~~~~ Re-load data:
rollout_eval = 1 # where to evaluate the RMSE
#vtk_file_test = 'datasets/BACKWARD_FACING_STEP/Backward_Facing_Step_Cropped_Re_32564.vtk'
vtk_file_test = 'datasets/BACKWARD_FACING_STEP/Backward_Facing_Step_Cropped_Re_26214.vtk'
path_to_ei = 'datasets/BACKWARD_FACING_STEP/edge_index'
path_to_ea = 'datasets/BACKWARD_FACING_STEP/edge_attr'
path_to_pos = 'datasets/BACKWARD_FACING_STEP/pos'
device_for_loading = device
use_radius = False
print('NOTE: USE_RADIUS = ', use_radius)
dataset_eval, _ = bfs.get_pygeom_dataset_cell_data(
vtk_file_test,
path_to_ei,
path_to_ea,
path_to_pos,
device_for_loading,
use_radius,
time_lag = rollout_eval,
scaling = [data_mean, data_std],
features_to_keep = [1,2],
fraction_valid = 0,
multiple_cases = False)
# Loop through test snapshots
N = len(dataset_eval)
# populate RMSE versus time plot
mse_full = []
mse_mask = [] # error in the masked region
for i in range(rollout_eval):
mse_full.append(np.zeros((N,2))) # 2 components for ux, uy
mse_mask.append(np.zeros((N,2))) # 2 components for ux, uy
for i in range(N):
print('Snapshot %d/%d' %(i+1, N))
data = dataset_eval[i]
x_new = data.x
for t in range(rollout_eval):
print('\tRollout %d/%d' %(t+1, rollout_eval))
# Get prediction
x_old = torch.clone(x_new)
x_src, mask = model_topk(x_old, data.edge_index, data.edge_attr, data.pos, data.batch)
x_new = x_old + x_src
target = data.y[t]
# Compute MSE budget
n_nodes = target.shape[0]
mse_total_0 = (1.0/n_nodes) * torch.sum( (x_new[:,0] - target[:,0])**2 )
mse_mask_0 = (1.0/n_nodes) * torch.sum( mask * ((x_new[:,0] - target[:,0])**2) )
mse_total_1 = (1.0/n_nodes) * torch.sum( (x_new[:,1] - target[:,1])**2 )
mse_mask_1 = (1.0/n_nodes) * torch.sum( mask * ((x_new[:,1] - target[:,1])**2) )
# Store
mse_full[t][i][0] = mse_total_0
mse_full[t][i][1] = mse_total_1
mse_mask[t][i][0] = mse_mask_0
mse_mask[t][i][1] = mse_mask_1
# Save mse data
print('SAVING...')
print('SAVING...')
print('SAVING...')
savepath = './outputs/postproc/budget_with_reg'
if not os.path.exists(savepath):
os.mkdir(savepath)
np.save(savepath + '/mse_full_%s.npy' %(model_topk.get_save_header()), mse_full)
np.save(savepath + '/mse_mask_%s.npy' %(model_topk.get_save_header()), mse_mask)
# Write data, no seeding effect, using new (larger) dataset:
if 1 == 1:
print('Writing budget data, using new (larger) dataset...')
rf_list = [4,8,16]
for rf in rf_list:
print('~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~')
print('TOP-K RF %d' %(rf))
print('~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~')
desc = 'no_budget_reg'
modelpath_topk = 'saved_models/big_data/dt_gnn_1em4/%s/NO_RADIUS_LR_1em5_pretrained_topk_unet_rollout_1_seed_65_down_topk_1_1_up_topk_1_factor_%d_hc_128_down_enc_2_2_2_up_enc_2_2_down_dec_2_2_2_up_dec_2_2_param_sharing_0.tar' %(desc,rf)
#desc = 'budget_reg_lam_0.0001'
#modelpath_topk = 'saved_models/big_data/dt_gnn_1em4/%s/NO_RADIUS_LR_1em5_BUDGET_REG_pretrained_topk_unet_rollout_1_seed_65_down_topk_1_1_up_topk_1_factor_%d_hc_128_down_enc_2_2_2_up_enc_2_2_down_dec_2_2_2_up_dec_2_2_param_sharing_0.tar' %(desc,rf)
#desc = 'budget_reg_lam_0.001'
#modelpath_topk = 'saved_models/big_data/dt_gnn_1em4/%s/NO_RADIUS_LR_1em5_BUDGET_REG_pretrained_topk_unet_rollout_1_seed_65_down_topk_1_1_up_topk_1_factor_%d_hc_128_down_enc_2_2_2_up_enc_2_2_down_dec_2_2_2_up_dec_2_2_param_sharing_0.tar' %(desc,rf)
#desc = 'budget_reg_lam_0.01'
#modelpath_topk = 'saved_models/big_data/dt_gnn_1em4/%s/NO_RADIUS_LR_1em5_BUDGET_REG_pretrained_topk_unet_rollout_1_seed_65_down_topk_1_1_up_topk_1_factor_%d_hc_128_down_enc_2_2_2_up_enc_2_2_down_dec_2_2_2_up_dec_2_2_param_sharing_0.tar' %(desc,rf)
p = torch.load(modelpath_topk)
input_dict = p['input_dict']
model_topk = gnn.GNN_TopK_NoReduction(
in_channels_node = input_dict['in_channels_node'],
in_channels_edge = input_dict['in_channels_edge'],
hidden_channels = input_dict['hidden_channels'],
out_channels = input_dict['out_channels'],
n_mlp_encode = input_dict['n_mlp_encode'],
n_mlp_mp = input_dict['n_mlp_mp'],
n_mp_down_topk = input_dict['n_mp_down_topk'],
n_mp_up_topk = input_dict['n_mp_up_topk'],
pool_ratios = input_dict['pool_ratios'],
n_mp_down_enc = input_dict['n_mp_down_enc'],
n_mp_up_enc = input_dict['n_mp_up_enc'],
n_mp_down_dec = input_dict['n_mp_down_dec'],
n_mp_up_dec = input_dict['n_mp_up_dec'],
lengthscales_enc = input_dict['lengthscales_enc'],
lengthscales_dec = input_dict['lengthscales_dec'],
bounding_box = input_dict['bounding_box'],
interpolation_mode = input_dict['interp'],
act = input_dict['act'],
param_sharing = input_dict['param_sharing'],
filter_lengthscale = input_dict['filter_lengthscale'],
name = input_dict['name'])
model_topk.load_state_dict(p['state_dict'])
model_topk.to(device)
model_topk.eval()
# ~~~~ Re-load data: ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
# Loading the new (big) data:
filenames = [] # this contains the vtk locations
data_dir = './datasets'
filenames = os.listdir(data_dir + '/BACKWARD_FACING_STEP/full/20_cases/')
filenames = sorted([item for item in filenames if 'Re_' in item])
filenames = filenames[1::2]
for Re_str in filenames:
print('\t%s' %(Re_str))
path_to_vtk_test = data_dir + '/BACKWARD_FACING_STEP/full/20_cases/' + Re_str + '/VTK/Backward_Facing_Step_0_final_smooth.vtk'
path_to_ei = data_dir + '/BACKWARD_FACING_STEP/full/edge_index'
path_to_ea = data_dir + '/BACKWARD_FACING_STEP/full/edge_attr'
path_to_pos = data_dir + '/BACKWARD_FACING_STEP/full/pos'
device_for_loading = device
use_radius = False
gnn_dt = 10
rollout_eval = 1
rollout_steps = rollout_eval
dataset_eval, _ = bfs.get_pygeom_dataset_cell_data(
path_to_vtk_test,
path_to_ei,
path_to_ea,
path_to_pos,
device_for_loading,
use_radius,
time_skip = gnn_dt,
time_lag = rollout_steps,
scaling = [data_mean, data_std],
features_to_keep = [1,2],
fraction_valid = 0,
multiple_cases = False)
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
# Loop through test snapshots
N = len(dataset_eval)
# populate RMSE versus time plot
mse_full = []
mse_mask = [] # error in the masked region
for i in range(rollout_eval):
mse_full.append(np.zeros((N,2))) # 2 components for ux, uy
mse_mask.append(np.zeros((N,2))) # 2 components for ux, uy
t_re = time.time()
for i in range(N):
#print('\t\tSnapshot %d/%d' %(i+1, N))
data = dataset_eval[i]
x_new = data.x
for t in range(rollout_eval):
#print('\t\t\tRollout %d/%d' %(t+1, rollout_eval))
# Get prediction
x_old = torch.clone(x_new)
x_src, mask = model_topk(x_old, data.edge_index, data.edge_attr, data.pos, data.batch)
x_new = x_old + x_src
target = data.y[t]
# Compute MSE budget
n_nodes = target.shape[0]
mse_total_0 = (1.0/n_nodes) * torch.sum( (x_new[:,0] - target[:,0])**2 )
mse_mask_0 = (1.0/n_nodes) * torch.sum( mask * ((x_new[:,0] - target[:,0])**2) )
mse_total_1 = (1.0/n_nodes) * torch.sum( (x_new[:,1] - target[:,1])**2 )
mse_mask_1 = (1.0/n_nodes) * torch.sum( mask * ((x_new[:,1] - target[:,1])**2) )
# Store
mse_full[t][i][0] = mse_total_0
mse_full[t][i][1] = mse_total_1
mse_mask[t][i][0] = mse_mask_0
mse_mask[t][i][1] = mse_mask_1
# Save mse data
#savepath = './outputs/postproc/big_data/%s/%s' %(desc, Re_str)
savepath = './outputs/postproc/for_paper/single_step_budget/%s/%s' %(desc, Re_str)
if not os.path.exists(savepath):
os.makedirs(savepath, exist_ok=True)
np.save(savepath + '/mse_full_%s.npy' %(model_topk.get_save_header()), mse_full)
np.save(savepath + '/mse_mask_%s.npy' %(model_topk.get_save_header()), mse_mask)
t_re = time.time() - t_re
print('\t\t took %g sec' %(t_re))
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
# Postprocess testing losses: RMSE
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
if 1 == 0:
print('postprocess testing losses.')
# set device
if torch.cuda.is_available():
device = 'cuda:0'
else:
device = 'cpu'
# Load models: effect of seed for R = 1
modelpath_list = []
# modelpath_list.append('saved_models/big_data/dt_gnn_1em4/baseline/NO_RADIUS_LR_1em5_topk_unet_rollout_1_seed_82_down_topk_2_up_topk_factor_4_hc_128_down_enc_2_2_2_up_enc_2_2_down_dec_2_2_2_up_dec_2_2_param_sharing_0.tar') # baseline, withn oise
# modelpath_list.append('saved_models/big_data/dt_gnn_1em4/no_budget_reg/NO_RADIUS_LR_1em5_pretrained_topk_unet_rollout_1_seed_65_down_topk_1_1_up_topk_1_factor_4_hc_128_down_enc_2_2_2_up_enc_2_2_down_dec_2_2_2_up_dec_2_2_param_sharing_0.tar') # rf=4, lam=0
# modelpath_list.append('saved_models/big_data/dt_gnn_1em4/no_budget_reg/NO_RADIUS_LR_1em5_pretrained_topk_unet_rollout_1_seed_65_down_topk_1_1_up_topk_1_factor_8_hc_128_down_enc_2_2_2_up_enc_2_2_down_dec_2_2_2_up_dec_2_2_param_sharing_0.tar') # rf=8, lam=0
# modelpath_list.append('saved_models/big_data/dt_gnn_1em4/no_budget_reg/NO_RADIUS_LR_1em5_pretrained_topk_unet_rollout_1_seed_65_down_topk_1_1_up_topk_1_factor_16_hc_128_down_enc_2_2_2_up_enc_2_2_down_dec_2_2_2_up_dec_2_2_param_sharing_0.tar') # rf=16, lam=0
# budget reg models
modelpath_list.append('saved_models/big_data/dt_gnn_1em4/budget_reg_lam_0.001/NO_RADIUS_LR_1em5_BUDGET_REG_pretrained_topk_unet_rollout_1_seed_65_down_topk_1_1_up_topk_1_factor_4_hc_128_down_enc_2_2_2_up_enc_2_2_down_dec_2_2_2_up_dec_2_2_param_sharing_0.tar') # rf = 4
modelpath_list.append('saved_models/big_data/dt_gnn_1em4/budget_reg_lam_0.001/NO_RADIUS_LR_1em5_BUDGET_REG_pretrained_topk_unet_rollout_1_seed_65_down_topk_1_1_up_topk_1_factor_8_hc_128_down_enc_2_2_2_up_enc_2_2_down_dec_2_2_2_up_dec_2_2_param_sharing_0.tar') # rf = 8
modelpath_list.append('saved_models/big_data/dt_gnn_1em4/budget_reg_lam_0.001/NO_RADIUS_LR_1em5_BUDGET_REG_pretrained_topk_unet_rollout_1_seed_65_down_topk_1_1_up_topk_1_factor_16_hc_128_down_enc_2_2_2_up_enc_2_2_down_dec_2_2_2_up_dec_2_2_param_sharing_0.tar') # rf = 16
# Load rmse data --(NEW -- for big data)
if 1 == 0:
rmse_path = './outputs/postproc/rmse_big_data_no_radius'
Re_list = sorted(os.listdir(rmse_path))
modelname_list = ['NO_RADIUS_LR_1em5_topk_unet_rollout_1_seed_82_down_topk_2_up_topk_factor_4_hc_128_down_enc_2_2_2_up_enc_2_2_down_dec_2_2_2_up_dec_2_2_param_sharing_0',
'NO_NOISE_NO_RADIUS_LR_1em5_topk_unet_rollout_1_seed_82_down_topk_2_up_topk_factor_4_hc_128_down_enc_2_2_2_up_enc_2_2_down_dec_2_2_2_up_dec_2_2_param_sharing_0']
rmse_list = [ [], [] ]
for i in range(len(modelname_list)):
for j in range(len(Re_list)):
re_dir = Re_list[j]
rmse_list[i].append( np.load(rmse_path + '/' + re_dir + '/%s.npy' %(modelname_list[i])) )
# compute average rmse:
rmse_mean_list = [ [], [] ]
rmse_max_list = [ [], [] ]
rmse_min_list = [ [], [] ]
for i in range(len(modelname_list)):
for j in range(len(Re_list)):
rmse_mean_list[i].append( np.mean(rmse_list[i][j], axis=1) )
rmse_max_list[i].append( np.max(rmse_list[i][j], axis=1) )
rmse_min_list[i].append( np.min(rmse_list[i][j], axis=1) )
rollout_steps = np.arange(1,rmse_list[0][0].shape[0] + 1)
# Fig, ax
lw = 0.5
ms = 10
mew = 1
cmap = plt.get_cmap('viridis')
colors = []
for i in range(len(Re_list)):
colors.append( cmap(i / len(Re_list)) )
fig, ax = plt.subplots(1,2, figsize=(14,5))
for j in range(len(Re_list)):
# ux -- baseline, with noise:
ax[0].plot(rollout_steps,
rmse_mean_list[0][j][:,0],#/rmse_mean_list[0][j][0,0],
label='Baseline', lw=lw, color=colors[j], #color='black',
marker='o', ms=ms, mew=mew, fillstyle='none')
ax[0].set_xlabel('Rollout Steps')
ax[0].set_ylabel('RMSE')
ax[0].set_title('Ux')
# ux -- baseline, no noise:
ax[0].plot(rollout_steps,
rmse_mean_list[1][j][:,0],#/rmse_mean_list[1][j][0,0],
label='Baseline', lw=lw, color=colors[j], #color='black',
marker='s', ms=ms, mew=mew, fillstyle='none')
ax[0].set_xlabel('Rollout Steps')
ax[0].set_ylabel('RMSE')
ax[0].set_title('Ux')
ax[0].set_yscale('log')
# uy -- baseline, with noise:
ax[1].plot(rollout_steps,
rmse_mean_list[0][j][:,1],#/rmse_mean_list[0][j][0,1],
label='Baseline', lw=lw, color=colors[j], #color='black',
marker='o', ms=ms, mew=mew, fillstyle='none')
ax[1].set_xlabel('Rollout Steps')
ax[1].set_ylabel('RMSE')
ax[1].set_title('Uy')
# uy -- baseline, no noise:
ax[1].plot(rollout_steps,
rmse_mean_list[1][j][:,1],#/rmse_mean_list[1][j][0,1],
label='Baseline', lw=lw, color=colors[j], #color='black',
marker='s', ms=ms, mew=mew, fillstyle='none')
ax[1].set_xlabel('Rollout Steps')
ax[1].set_ylabel('RMSE')
ax[1].set_title('Uy')
ax[1].set_yscale('log')
plt.show(block=False)
# Load rmse data -- effect of seed (OLD):
if 1 == 0:
rmse_path = './outputs/postproc/rmse_data_no_radius/Re_26214/'
#rmse_path = './outputs/postproc/rmse_data_no_radius/Re_32564/'
rmse_baseline = np.load(rmse_path + 'NO_RADIUS_LR_1em5_topk_unet_rollout_1_seed_82_down_topk_2_up_topk_factor_4_hc_128_down_enc_2_2_2_up_enc_2_2_down_dec_2_2_2_up_dec_2_2_param_sharing_0.npy')
rmse_topk_mean = []
for seed in seed_list:
#rmse_topk_seed = np.load(rmse_path + 'NO_RADIUS_LR_1em5_pretrained_topk_unet_rollout_1_seed_%d_down_topk_1_1_up_topk_1_factor_4_hc_128_down_enc_2_2_2_up_enc_2_2_down_dec_2_2_2_up_dec_2_2_param_sharing_0.npy' %(seed))
rmse_topk_seed = np.load(rmse_path + 'NO_RADIUS_LR_1em5_BUDGET_REG_pretrained_topk_unet_rollout_1_seed_%d_down_topk_1_1_up_topk_1_factor_4_hc_128_down_enc_2_2_2_up_enc_2_2_down_dec_2_2_2_up_dec_2_2_param_sharing_0.npy' %(seed))
rmse_topk_mean.append(np.mean(rmse_topk_seed, axis=1))
# compute average rmse:
rmse_baseline_mean = np.mean(rmse_baseline, axis=1)
rmse_baseline_max = np.max(rmse_baseline, axis=1)
rmse_baseline_min = np.min(rmse_baseline, axis=1)
rollout_steps = np.arange(1,rmse_baseline.shape[0] + 1)
# Fig, ax
lw = 2
ms = 1
mew = 1
fig, ax = plt.subplots(1,2, figsize=(14,5))
# ux:
ax[0].plot(rollout_steps, rmse_baseline_mean[:,0], label='Baseline', lw=lw, color='black')
for seed_id in range(len(seed_list)):
ax[0].plot(rollout_steps, rmse_topk_mean[seed_id][:,0], lw=lw, color='red', zorder=-1)
if seed_id == 0:
ax[0].plot(rollout_steps, rmse_topk_mean[seed_id][:,0], lw=lw+2, color='lime', zorder=-1)
ax[0].set_xlabel('Rollout Steps')
ax[0].set_ylabel('RMSE')
ax[0].set_title('Ux')
# uy:
ax[1].plot(rollout_steps, rmse_baseline_mean[:,1], label='Baseline', lw=lw, color='black')
for seed_id in range(len(seed_list)):
ax[1].plot(rollout_steps, rmse_topk_mean[seed_id][:,1], lw=lw, color='red', zorder=-1)
if seed_id == 0:
ax[1].plot(rollout_steps, rmse_topk_mean[seed_id][:,1], lw=lw+2, color='lime', zorder=-1)
ax[1].set_xlabel('Rollout Steps')
ax[1].set_ylabel('RMSE')
ax[1].set_title('Uy')
#ax[0].set_ylim([1e-3, 1e-1])
#ax[1].set_ylim([1e-3, 1e-1])
#ax[0].set_yscale('log')
#ax[1].set_yscale('log')
#ax[0].legend(framealpha=1, fancybox=False, edgecolor='black', prop={'size': 14})
plt.show(block=False)
# Write data:
if 1 == 1:
for modelpath in modelpath_list:
p = torch.load(modelpath)
input_dict = p['input_dict']
# with top-k, no reduction
model = gnn.GNN_TopK_NoReduction(
in_channels_node = input_dict['in_channels_node'],
in_channels_edge = input_dict['in_channels_edge'],
hidden_channels = input_dict['hidden_channels'],
out_channels = input_dict['out_channels'],
n_mlp_encode = input_dict['n_mlp_encode'],
n_mlp_mp = input_dict['n_mlp_mp'],
n_mp_down_topk = input_dict['n_mp_down_topk'],
n_mp_up_topk = input_dict['n_mp_up_topk'],
pool_ratios = input_dict['pool_ratios'],
n_mp_down_enc = input_dict['n_mp_down_enc'],
n_mp_up_enc = input_dict['n_mp_up_enc'],
n_mp_down_dec = input_dict['n_mp_down_dec'],
n_mp_up_dec = input_dict['n_mp_up_dec'],
lengthscales_enc = input_dict['lengthscales_enc'],
lengthscales_dec = input_dict['lengthscales_dec'],
bounding_box = input_dict['bounding_box'],
interpolation_mode = input_dict['interp'],
act = input_dict['act'],
param_sharing = input_dict['param_sharing'],
filter_lengthscale = input_dict['filter_lengthscale'],
name = input_dict['name'])
model.load_state_dict(p['state_dict'])
model.to(device)
model.eval()
# ~~~~ Re-load data:
rollout_eval = 1 # where to evaluate the RMSE
# ~~~~ # Loading the old (small) data:
# vtk_file_test = 'datasets/BACKWARD_FACING_STEP/Backward_Facing_Step_Cropped_Re_32564.vtk'
# #vtk_file_test = 'datasets/BACKWARD_FACING_STEP/Backward_Facing_Step_Cropped_Re_26214.vtk'
# path_to_ei = 'datasets/BACKWARD_FACING_STEP/edge_index'
# path_to_ea = 'datasets/BACKWARD_FACING_STEP/edge_attr'
# path_to_pos = 'datasets/BACKWARD_FACING_STEP/pos'
# device_for_loading = device
# use_radius = False
# print('NOTE: USE_RADIUS = ', use_radius)
# dataset_eval_rmse, _ = bfs.get_pygeom_dataset_cell_data(
# vtk_file_test,
# path_to_ei,
# path_to_ea,
# path_to_pos,
# device_for_loading,
# use_radius,
# time_lag = rollout_eval,
# scaling = [data_mean, data_std],
# features_to_keep = [1,2],
# fraction_valid = 0,
# multiple_cases = False)
# Loading the new (big) data:
filenames = [] # this contains the vtk locations
data_dir = './datasets'
filenames = os.listdir(data_dir + '/BACKWARD_FACING_STEP/full/20_cases/')
filenames = sorted([item for item in filenames if 'Re_' in item])
filenames_train = filenames[::2]
filenames_test = filenames[1::2]
for item in filenames_test:
path_to_vtk_test = data_dir + '/BACKWARD_FACING_STEP/full/20_cases/' + item + '/VTK/Backward_Facing_Step_0_final_smooth.vtk'
path_to_ei = data_dir + '/BACKWARD_FACING_STEP/full/edge_index'
path_to_ea = data_dir + '/BACKWARD_FACING_STEP/full/edge_attr'
path_to_pos = data_dir + '/BACKWARD_FACING_STEP/full/pos'
device_for_loading = device
use_radius = False
gnn_dt = 10
rollout_steps = rollout_eval
dataset_eval_rmse, _ = bfs.get_pygeom_dataset_cell_data(
path_to_vtk_test,
path_to_ei,
path_to_ea,
path_to_pos,
device_for_loading,
use_radius,
time_skip = gnn_dt,
time_lag = rollout_steps,
scaling = [data_mean, data_std],
features_to_keep = [1,2],
fraction_valid = 0,
multiple_cases = False)
# Loop through test snapshots
N = len(dataset_eval_rmse)
# populate RMSE versus time plot
rmse_data = []
for i in range(rollout_eval):
rmse_data.append(np.zeros((N,2))) # 2 components for ux, uy
for i in range(N): # skip first 20 snapshots
print('%s, Snapshot %d/%d' %(item, i+1, N))
data = dataset_eval_rmse[i]
x_new = data.x
for t in range(rollout_eval):
print('\tRollout %d/%d' %(t+1, rollout_eval))
x_old = torch.clone(x_new)
x_src,_ = model(x_old, data.edge_index, data.edge_attr, data.pos, data.batch)
x_new = x_old + x_src
target = data.y[t]
# unscale prediction and target
n_features = x_old.shape[1]
mean_i = data.data_scale[0].reshape((1,n_features))
std_i = data.data_scale[1].reshape((1,n_features))
x_new = x_new * std_i + mean_i
target = target * std_i + mean_i
# compute rmse
rmse_data[t][i][0] = torch.sqrt(F.mse_loss(x_new[:,0], target[:,0]))
rmse_data[t][i][1] = torch.sqrt(F.mse_loss(x_new[:,1], target[:,1]))
# Save rmse_data
savepath = './outputs/postproc/for_paper/single_step_rmse/%s' %(item)
if not os.path.exists(savepath):
os.makedirs(savepath)
np.save(savepath + '/%s.npy' %(model.get_save_header()), rmse_data)
print('Saved at: %s' %(savepath + '/%s.npy' %(model.get_save_header())))
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
# Write model predictions -- Small trajectories, for assessing rollout accuracy (FOR PAPER)
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
if 1 == 1:
print('Write model predictions, small trajectories...')
if torch.cuda.is_available():
device = 'cuda:0'
else:
device = 'cpu'
header_list = ['no_budget_reg', 'budget_reg_lam_0.0001', 'budget_reg_lam_0.001', 'budget_reg_lam_0.01', 'baseline']
header_list = ['budget_reg_lam_test_no_inv_budget']
header_list = ['baseline']
header_list = ['no_budget_reg']
for header in header_list:
modelpath = './saved_models/big_data/dt_gnn_1em4/' + header
temp = os.listdir(modelpath)
temp = ['TOPK_RELU_NO_RADIUS_LR_1em5_pretrained_topk_unet_rollout_1_seed_65_down_topk_1_1_up_topk_1_factor_16_hc_128_down_enc_2_2_2_up_enc_2_2_down_dec_2_2_2_up_dec_2_2_param_sharing_0.tar']
modelpath_list = [modelpath + '/' + item for item in temp]
for modelpath in modelpath_list: # loops through RF
p = torch.load(modelpath)
input_dict = p['input_dict']
print('input_dict: ', input_dict)
# with top-k, no reduction
model = gnn.GNN_TopK_NoReduction(
in_channels_node = input_dict['in_channels_node'],
in_channels_edge = input_dict['in_channels_edge'],
hidden_channels = input_dict['hidden_channels'],
out_channels = input_dict['out_channels'],
n_mlp_encode = input_dict['n_mlp_encode'],
n_mlp_mp = input_dict['n_mlp_mp'],
n_mp_down_topk = input_dict['n_mp_down_topk'],
n_mp_up_topk = input_dict['n_mp_up_topk'],
pool_ratios = input_dict['pool_ratios'],
n_mp_down_enc = input_dict['n_mp_down_enc'],
n_mp_up_enc = input_dict['n_mp_up_enc'],
n_mp_down_dec = input_dict['n_mp_down_dec'],
n_mp_up_dec = input_dict['n_mp_up_dec'],
lengthscales_enc = input_dict['lengthscales_enc'],
lengthscales_dec = input_dict['lengthscales_dec'],
bounding_box = input_dict['bounding_box'],
interpolation_mode = input_dict['interp'],
act = input_dict['act'],
param_sharing = input_dict['param_sharing'],
filter_lengthscale = input_dict['filter_lengthscale'],
name = input_dict['name'])
model.load_state_dict(p['state_dict'])
model.to(device)
model.eval()
model_save_header = model.get_save_header()
# Loading the new (big) data:
Re_list = [] # this contains the vtk locations
data_dir = './datasets'
Re_list = os.listdir(data_dir + '/BACKWARD_FACING_STEP/full/20_cases/')
Re_list = sorted([item for item in Re_list if 'Re_' in item])
Re_test = Re_list[1::2]
Re_test = ['Re_27233', 'Re_35392', 'Re_45589']
#Re_test = ['Re_45589']
#Re_test = ['Re_35392']
for Re_str in Re_test: # loops through Re_test
print('\t%s' %(Re_str))
path_to_vtk_test = data_dir + '/BACKWARD_FACING_STEP/full/20_cases/' + Re_str + '/VTK/Backward_Facing_Step_0_final_smooth.vtk'
path_to_ei = data_dir + '/BACKWARD_FACING_STEP/full/edge_index'
path_to_ea = data_dir + '/BACKWARD_FACING_STEP/full/edge_attr'
path_to_pos = data_dir + '/BACKWARD_FACING_STEP/full/pos'
device_for_loading = device
use_radius = False
gnn_dt = 10
rollout_steps = 50
#rollout_steps = 300