diff --git a/DeepINN/model.py b/DeepINN/model.py index a205c3d..3faf280 100644 --- a/DeepINN/model.py +++ b/DeepINN/model.py @@ -1,7 +1,8 @@ import torch import sys from .backend import loss_metric, choose_optimiser -from .config import Config +from .config import Config +from .utils import timer class Model(): """ @@ -46,8 +47,7 @@ def compile_network(self): ) print("Network compiled", file=sys.stderr, flush=True) - def train(self, iterations : int = None, display_every : int = None): - + def initialise_training(self, iterations : int = None): if self.iter == 0: # We are running a fresh training self.training_history = [] # Initialize an empty list for storing loss values self.iterations = iterations @@ -65,6 +65,18 @@ def train(self, iterations : int = None, display_every : int = None): # Set requires_grad=True for self.collocation_point_sample self.collocation_point_sample.requires_grad = True + def train(self, iterations : int = None, display_every : int = 1): + """_summary_ + + Args: + iterations (int): _description_. Number of iterations. + display_every (int, optional): _description_. Display the loss every display_every iterations. Defaults to 1. + """ + self.initialise_training(iterations) + self.trainer() + + @timer + def trainer(self): # implement training loop while self.iter <= self.iterations: @@ -93,6 +105,4 @@ def train(self, iterations : int = None, display_every : int = None): self.iter = self.iter + 1 else: print('Training finished') - #elapsed = time.time() - start_time - #print('Training time: %.2f' % (elapsed)) - #print(f"Final loss: {total_loss}") \ No newline at end of file + diff --git a/DeepINN/utils/__init__.py b/DeepINN/utils/__init__.py index 9536e21..59cae11 100644 --- a/DeepINN/utils/__init__.py +++ b/DeepINN/utils/__init__.py @@ -1,12 +1,3 @@ -"""Useful helper methods for the definition and evaluation of a problem. - -For the creation of conditions, some differential operators are implemented under -torchphysics.utils.differentialoperators. - -For the evaluation of the trained model, some plot and animation functionalities are provided. -They can give you a rough overview of the determined solution. These lay under -torchphysics.utils.plotting -""" from .differentialoperators import (laplacian, grad, div, @@ -20,7 +11,7 @@ from .data import PointsDataset, PointsDataLoader, DeepONetDataLoader -from .user_fun import UserFunction, tensor2numpy +from .user_fun import UserFunction, tensor2numpy, timer from .plotting import plot, animate, scatter from .evaluation import compute_min_and_max diff --git a/DeepINN/utils/user_fun.py b/DeepINN/utils/user_fun.py index 66aeb09..22a37e0 100644 --- a/DeepINN/utils/user_fun.py +++ b/DeepINN/utils/user_fun.py @@ -5,6 +5,8 @@ import inspect import copy import torch +import functools +import time from ..geometry.spaces.points import Points @@ -317,3 +319,14 @@ def tensor2numpy(tensor_list): Converts a list of torch.tensors to numpy arrays. """ return [tensor.detach().cpu().numpy() for tensor in tensor_list] + +def timer(func): + """Print the runtime of the decorated function""" + @functools.wraps(func) + def wrapper_timer(*args, **kwargs): + start_time = time.perf_counter() + func(*args, **kwargs) # execute the decorated function + end_time = time.perf_counter() + run_time = end_time - start_time + print(f"Time taken: {func.__name__!r} in {run_time:.4f} secs") + return wrapper_timer diff --git a/Tutorials/5. FCNN/3. model.ipynb b/Tutorials/5. FCNN/3. model.ipynb index 0ec7bcc..85416dd 100644 --- a/Tutorials/5. FCNN/3. model.ipynb +++ b/Tutorials/5. FCNN/3. model.ipynb @@ -198,18 +198,19 @@ "name": "stdout", "output_type": "stream", "text": [ - "Iteration: 1 \t BC Loss: 0.4149\t PDE Loss: 0.0000 \t Loss: 0.4149\n", - "Iteration: 51 \t BC Loss: 0.3126\t PDE Loss: 0.0000 \t Loss: 0.3126\n", - "Iteration: 101 \t BC Loss: 0.2269\t PDE Loss: 0.0000 \t Loss: 0.2269\n", - "Iteration: 151 \t BC Loss: 0.1661\t PDE Loss: 0.0000 \t Loss: 0.1661\n", - "Iteration: 201 \t BC Loss: 0.1287\t PDE Loss: 0.0000 \t Loss: 0.1287\n", - "Iteration: 251 \t BC Loss: 0.1055\t PDE Loss: 0.0000 \t Loss: 0.1055\n", - "Iteration: 301 \t BC Loss: 0.0878\t PDE Loss: 0.0000 \t Loss: 0.0878\n", - "Iteration: 351 \t BC Loss: 0.0716\t PDE Loss: 0.0000 \t Loss: 0.0716\n", - "Iteration: 401 \t BC Loss: 0.0561\t PDE Loss: 0.0000 \t Loss: 0.0561\n", - "Iteration: 451 \t BC Loss: 0.0420\t PDE Loss: 0.0000 \t Loss: 0.0420\n", - "Iteration: 501 \t BC Loss: 0.0298\t PDE Loss: 0.0000 \t Loss: 0.0298\n", - "Training finished\n" + "Iteration: 1 \t BC Loss: 0.4354\t PDE Loss: 0.0000 \t Loss: 0.4354\n", + "Iteration: 51 \t BC Loss: 0.2866\t PDE Loss: 0.0000 \t Loss: 0.2866\n", + "Iteration: 101 \t BC Loss: 0.1963\t PDE Loss: 0.0000 \t Loss: 0.1963\n", + "Iteration: 151 \t BC Loss: 0.1437\t PDE Loss: 0.0000 \t Loss: 0.1437\n", + "Iteration: 201 \t BC Loss: 0.1102\t PDE Loss: 0.0000 \t Loss: 0.1102\n", + "Iteration: 251 \t BC Loss: 0.0852\t PDE Loss: 0.0000 \t Loss: 0.0852\n", + "Iteration: 301 \t BC Loss: 0.0650\t PDE Loss: 0.0000 \t Loss: 0.0650\n", + "Iteration: 351 \t BC Loss: 0.0485\t PDE Loss: 0.0000 \t Loss: 0.0485\n", + "Iteration: 401 \t BC Loss: 0.0352\t PDE Loss: 0.0000 \t Loss: 0.0352\n", + "Iteration: 451 \t BC Loss: 0.0249\t PDE Loss: 0.0000 \t Loss: 0.0249\n", + "Iteration: 501 \t BC Loss: 0.0172\t PDE Loss: 0.0000 \t Loss: 0.0172\n", + "Training finished\n", + "Time taken: 'trainer' in 4.2741 secs\n" ] } ], @@ -255,7 +256,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 14, "metadata": {}, "outputs": [], "source": [ @@ -265,7 +266,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 15, "metadata": {}, "outputs": [], "source": [ @@ -274,7 +275,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 16, "metadata": {}, "outputs": [], "source": [ @@ -283,12 +284,12 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 17, "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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", 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" ] @@ -306,7 +307,7 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 18, "metadata": {}, "outputs": [ { @@ -315,13 +316,13 @@ "Text(0, 0.5, 'Loss')" ] }, - "execution_count": 23, + "execution_count": 18, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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", 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", 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" ] @@ -354,7 +355,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.10" + "version": "3.9.16" }, "orig_nbformat": 4 }, diff --git a/docs/requirements.txt b/docs/requirements.txt index 7e821e4..5235316 100644 --- a/docs/requirements.txt +++ b/docs/requirements.txt @@ -1,3 +1,4 @@ jupyter-book matplotlib numpy +ghp-import