From fdb8f65143c424816d204f397c8ba23e5eced9ab Mon Sep 17 00:00:00 2001 From: Dario Coscia <93731561+dario-coscia@users.noreply.github.com> Date: Fri, 4 Oct 2024 15:59:09 +0200 Subject: [PATCH] Filippo0.2 (#361) * Add summation and remove deepcopy (only for tensors) in LabelTensor class * Update operators for compatibility with updated LabelTensor implementation * Implement labels.setter in LabelTensor class * Update LabelTensor --------- Co-authored-by: FilippoOlivo --- pina/label_tensor.py | 108 ++++++++++++++++++++++++++--------- pina/operators.py | 114 +++++++++++++++++++++---------------- tests/test_label_tensor.py | 58 ++++++++++++++++++- tests/test_operators.py | 24 ++++---- 4 files changed, 212 insertions(+), 92 deletions(-) diff --git a/pina/label_tensor.py b/pina/label_tensor.py index 7646dd8..08e0b03 100644 --- a/pina/label_tensor.py +++ b/pina/label_tensor.py @@ -35,14 +35,34 @@ class LabelTensor(torch.Tensor): {1: {"name": "space"['a', 'b', 'c']) """ - self.labels = None + self.labels = labels + + @property + def labels(self): + """Property decorator for labels + + :return: labels of self + :rtype: list + """ + return self._labels + + @labels.setter + def labels(self, labels): + """" + Set properly the parameter _labels + + :param labels: Labels to assign to the class variable _labels. + :type: labels: str | list(str) | dict + """ + if hasattr(self, 'labels') is False: + self.init_labels() if isinstance(labels, dict): - self.update_labels(labels) + self.update_labels_from_dict(labels) elif isinstance(labels, list): - self.init_labels_from_list(labels) + self.update_labels_from_list(labels) elif isinstance(labels, str): labels = [labels] - self.init_labels_from_list(labels) + self.update_labels_from_list(labels) else: raise ValueError(f"labels must be list, dict or string.") @@ -60,38 +80,38 @@ class LabelTensor(torch.Tensor): if isinstance(label_to_extract, (str, int)): label_to_extract = [label_to_extract] if isinstance(label_to_extract, (tuple, list)): - last_dim_label = self.labels[self.tensor.ndim - 1]['dof'] + last_dim_label = self._labels[self.tensor.ndim - 1]['dof'] if set(label_to_extract).issubset(last_dim_label) is False: raise ValueError('Cannot extract a dof which is not in the original LabelTensor') idx_to_extract = [last_dim_label.index(i) for i in label_to_extract] - new_tensor = deepcopy(self.tensor) + new_tensor = self.tensor new_tensor = new_tensor[..., idx_to_extract] - new_labels = deepcopy(self.labels) + new_labels = deepcopy(self._labels) last_dim_new_label = {self.tensor.ndim - 1: { 'dof': label_to_extract, - 'name': self.labels[self.tensor.ndim - 1]['name'] + 'name': self._labels[self.tensor.ndim - 1]['name'] }} new_labels.update(last_dim_new_label) elif isinstance(label_to_extract, dict): - new_labels = (deepcopy(self.labels)) - new_tensor = deepcopy(self.tensor) + new_labels = (deepcopy(self._labels)) + new_tensor = self.tensor for k, v in label_to_extract.items(): idx_dim = None - for kl, vl in self.labels.items(): + for kl, vl in self._labels.items(): if vl['name'] == k: idx_dim = kl break - dim_labels = self.labels[idx_dim]['dof'] + dim_labels = self._labels[idx_dim]['dof'] if isinstance(label_to_extract[k], (int, str)): label_to_extract[k] = [label_to_extract[k]] if set(label_to_extract[k]).issubset(dim_labels) is False: - raise ValueError('Cannot extract a dof which is not in the original labeltensor') + raise ValueError('Cannot extract a dof which is not in the original LabelTensor') idx_to_extract = [dim_labels.index(i) for i in label_to_extract[k]] indexer = [slice(None)] * idx_dim + [idx_to_extract] + [slice(None)] * (self.tensor.ndim - idx_dim - 1) new_tensor = new_tensor[indexer] dim_new_label = {idx_dim: { 'dof': label_to_extract[k], - 'name': self.labels[idx_dim]['name'] + 'name': self._labels[idx_dim]['name'] }} new_labels.update(dim_new_label) else: @@ -104,7 +124,7 @@ class LabelTensor(torch.Tensor): """ s = '' - for key, value in self.labels.items(): + for key, value in self._labels.items(): s += f"{key}: {value}\n" s += '\n' s += super().__str__() @@ -155,7 +175,7 @@ class LabelTensor(torch.Tensor): def requires_grad_(self, mode=True): lt = super().requires_grad_(mode) - lt.labels = self.labels + lt.labels = self._labels return lt @property @@ -181,10 +201,19 @@ class LabelTensor(torch.Tensor): :rtype: LabelTensor """ - out = LabelTensor(super().clone(*args, **kwargs), self.labels) + out = LabelTensor(super().clone(*args, **kwargs), self._labels) return out - def update_labels(self, labels): + + def init_labels(self): + self._labels = { + idx_: { + 'dof': range(self.tensor.shape[idx_]), + 'name': idx_ + } for idx_ in range(self.tensor.ndim) + } + + def update_labels_from_dict(self, labels): """ Update the internal label representation according to the values passed as input. @@ -192,21 +221,16 @@ class LabelTensor(torch.Tensor): :type labels: dict :raises ValueError: dof list contain duplicates or number of dof does not match with tensor shape """ - self.labels = { - idx_: { - 'dof': range(self.tensor.shape[idx_]), - 'name': idx_ - } for idx_ in range(self.tensor.ndim) - } + tensor_shape = self.tensor.shape for k, v in labels.items(): if len(v['dof']) != len(set(v['dof'])): raise ValueError("dof must be unique") if len(v['dof']) != tensor_shape[k]: raise ValueError('Number of dof does not match with tensor dimension') - self.labels.update(labels) + self._labels.update(labels) - def init_labels_from_list(self, labels): + def update_labels_from_list(self, labels): """ Given a list of dof, this method update the internal label representation @@ -214,4 +238,34 @@ class LabelTensor(torch.Tensor): :type labels: list """ last_dim_labels = {self.tensor.ndim - 1: {'dof': labels, 'name': self.tensor.ndim - 1}} - self.update_labels(last_dim_labels) \ No newline at end of file + self.update_labels_from_dict(last_dim_labels) + + @staticmethod + def summation(tensors): + if len(tensors) == 0: + raise ValueError('tensors list must not be empty') + if len(tensors) == 1: + return tensors[0] + labels = tensors[0].labels + for j in range(tensors[0].ndim): + for i in range(1, len(tensors)): + if labels[j] != tensors[i].labels[j]: + labels.pop(j) + break + + data = torch.zeros(tensors[0].tensor.shape) + for i in range(len(tensors)): + data += tensors[i].tensor + new_tensor = LabelTensor(data, labels) + return new_tensor + + def last_dim_dof(self): + return self._labels[self.tensor.ndim - 1]['dof'] + + def append(self, tensor, mode='std'): + print(self.labels) + print(tensor.labels) + if mode == 'std': + new_label_tensor = LabelTensor.cat([self, tensor], dim=self.tensor.ndim - 1) + + return new_label_tensor diff --git a/pina/operators.py b/pina/operators.py index e523ed9..082d725 100644 --- a/pina/operators.py +++ b/pina/operators.py @@ -1,13 +1,13 @@ """ Module for operators vectorize implementation. Differential operators are used to write any differential problem. -These operators are implemented to work on different accellerators: CPU, GPU, TPU or MPS. +These operators are implemented to work on different accelerators: CPU, GPU, TPU or MPS. All operators take as input a tensor onto which computing the operator, a tensor with respect to which computing the operator, the name of the output variables to calculate the operator for (in case of multidimensional functions), and the variables name on which the operator is calculated. """ import torch - +from copy import deepcopy from pina.label_tensor import LabelTensor @@ -49,12 +49,12 @@ def grad(output_, input_, components=None, d=None): :rtype: LabelTensor """ - if len(output_.labels) != 1: + if len(output_.labels[output_.tensor.ndim-1]['dof']) != 1: raise RuntimeError("only scalar function can be differentiated") - if not all([di in input_.labels for di in d]): + if not all([di in input_.labels[input_.tensor.ndim-1]['dof'] for di in d]): raise RuntimeError("derivative labels missing from input tensor") - output_fieldname = output_.labels[0] + output_fieldname = output_.labels[output_.ndim-1]['dof'][0] gradients = torch.autograd.grad( output_, input_, @@ -65,41 +65,35 @@ def grad(output_, input_, components=None, d=None): retain_graph=True, allow_unused=True, )[0] - - gradients.labels = input_.labels + new_labels = deepcopy(input_.labels) + gradients.labels = new_labels gradients = gradients.extract(d) - gradients.labels = [f"d{output_fieldname}d{i}" for i in d] - + new_labels[input_.tensor.ndim - 1]['dof'] = [f"d{output_fieldname}d{i}" for i in d] + gradients.labels = new_labels return gradients if not isinstance(input_, LabelTensor): raise TypeError - if d is None: - d = input_.labels + d = input_.labels[input_.tensor.ndim-1]['dof'] if components is None: - components = output_.labels + components = output_.labels[output_.tensor.ndim-1]['dof'] - if output_.shape[1] == 1: # scalar output ################################ + if output_.shape[output_.ndim-1] == 1: # scalar output ################################ - if components != output_.labels: + if components != output_.labels[output_.tensor.ndim-1]['dof']: raise RuntimeError gradients = grad_scalar_output(output_, input_, d) - elif output_.shape[1] >= 2: # vector output ############################## - + elif output_.shape[output_.ndim-1] >= 2: # vector output ############################## + tensor_to_cat = [] for i, c in enumerate(components): c_output = output_.extract([c]) - if i == 0: - gradients = grad_scalar_output(c_output, input_, d) - else: - gradients = gradients.append( - grad_scalar_output(c_output, input_, d) - ) + tensor_to_cat.append(grad_scalar_output(c_output, input_, d)) + gradients = LabelTensor.cat(tensor_to_cat, dim=output_.tensor.ndim-1) else: raise NotImplementedError - return gradients @@ -130,27 +124,29 @@ def div(output_, input_, components=None, d=None): raise TypeError if d is None: - d = input_.labels + d = input_.labels[input_.tensor.ndim-1]['dof'] if components is None: - components = output_.labels + components = output_.labels[output_.tensor.ndim-1]['dof'] - if output_.shape[1] < 2 or len(components) < 2: + if output_.shape[output_.ndim-1] < 2 or len(components) < 2: raise ValueError("div supported only for vector fields") if len(components) != len(d): raise ValueError grad_output = grad(output_, input_, components, d) - div = torch.zeros(input_.shape[0], 1, device=output_.device) - labels = [None] * len(components) + last_dim_dof = [None] * len(components) + to_sum_tensors = [] for i, (c, d) in enumerate(zip(components, d)): c_fields = f"d{c}d{d}" - div[:, 0] += grad_output.extract(c_fields).sum(axis=1) - labels[i] = c_fields + last_dim_dof[i] = c_fields + to_sum_tensors.append(grad_output.extract(c_fields)) - div = div.as_subclass(LabelTensor) - div.labels = ["+".join(labels)] + div = LabelTensor.summation(to_sum_tensors) + new_labels = deepcopy(input_.labels) + new_labels[input_.tensor.ndim-1]['dof'] = ["+".join(last_dim_dof)] + div.labels = new_labels return div @@ -205,10 +201,10 @@ def laplacian(output_, input_, components=None, d=None, method="std"): return result if d is None: - d = input_.labels + d = input_.labels[input_.tensor.ndim-1]['dof'] if components is None: - components = output_.labels + components = output_.labels[output_.tensor.ndim-1]['dof'] if method == "divgrad": raise NotImplementedError("divgrad not implemented as method") @@ -218,25 +214,43 @@ def laplacian(output_, input_, components=None, d=None, method="std"): elif method == "std": if len(components) == 1: - result = scalar_laplace(output_, input_, components, d) + # result = scalar_laplace(output_, input_, components, d) # TODO check (from 0.1) + grad_output = grad(output_, input_, components=components, d=d) + to_append_tensors = [] + for i, label in enumerate(grad_output.labels[grad_output.ndim-1]['dof']): + gg = grad(grad_output, input_, d=d, components=[label]) + to_append_tensors.append(gg.extract([gg.labels[gg.tensor.ndim-1]['dof'][i]])) labels = [f"dd{components[0]}"] - + result = LabelTensor.summation(tensors=to_append_tensors) + result.labels = labels else: - result = torch.empty( - size=(input_.shape[0], len(components)), - dtype=output_.dtype, - device=output_.device, - ) - labels = [None] * len(components) - for idx, c in enumerate(components): - result[:, idx] = scalar_laplace(output_, input_, c, d).flatten() - labels[idx] = f"dd{c}" + # result = torch.empty( # TODO check (from 0.1) + # size=(input_.shape[0], len(components)), + # dtype=output_.dtype, + # device=output_.device, + # ) + # labels = [None] * len(components) + # for idx, c in enumerate(components): + # result[:, idx] = scalar_laplace(output_, input_, c, d).flatten() + # labels[idx] = f"dd{c}" - result = result.as_subclass(LabelTensor) - result.labels = labels + # result = result.as_subclass(LabelTensor) + # result.labels = labels + labels = [None] * len(components) + to_append_tensors = [None] * len(components) + for idx, (ci, di) in enumerate(zip(components, d)): + if not isinstance(ci, list): + ci = [ci] + if not isinstance(di, list): + di = [di] + grad_output = grad(output_, input_, components=ci, d=di) + to_append_tensors[idx] = grad(grad_output, input_, d=di) + labels[idx] = f"dd{ci[0]}dd{di[0]}" + result = LabelTensor.cat(tensors=to_append_tensors, dim=output_.tensor.ndim-1) + result.labels = labels return result - +# TODO Fix advection operator def advection(output_, input_, velocity_field, components=None, d=None): """ Perform advection operation. The operator works for vectorial functions, @@ -258,10 +272,10 @@ def advection(output_, input_, velocity_field, components=None, d=None): :rtype: LabelTensor """ if d is None: - d = input_.labels + d = input_.labels[input_.tensor.ndim-1]['dof'] if components is None: - components = output_.labels + components = output_.labels[output_.tensor.ndim-1]['dof'] tmp = ( grad(output_, input_, components, d) diff --git a/tests/test_label_tensor.py b/tests/test_label_tensor.py index f87d3ab..6ef484f 100644 --- a/tests/test_label_tensor.py +++ b/tests/test_label_tensor.py @@ -17,12 +17,14 @@ labels_row = { "dof": range(20) } } +labels_list = ['x', 'y', 'z'] labels_all = labels_column | labels_row -@pytest.mark.parametrize("labels", [labels_column, labels_row, labels_all]) +@pytest.mark.parametrize("labels", [labels_column, labels_row, labels_all, labels_list]) def test_constructor(labels): LabelTensor(data, labels) + def test_wrong_constructor(): with pytest.raises(ValueError): LabelTensor(data, ['a', 'b']) @@ -61,7 +63,6 @@ def test_extract_2D(labels_te): assert torch.all(torch.isclose(data[2,2].reshape(1, 1), new)) def test_extract_3D(): - labels = labels_all data = torch.rand(20, 3, 4) labels = { 1: { @@ -80,6 +81,7 @@ def test_extract_3D(): tensor = LabelTensor(data, labels) new = tensor.extract(labels_te) + tensor2 = LabelTensor(data, labels) assert new.ndim == tensor.ndim assert new.shape[0] == 20 assert new.shape[1] == 2 @@ -88,6 +90,10 @@ def test_extract_3D(): data[:, 0::2, 1:4].reshape(20, 2, 3), new )) + assert tensor2.ndim == tensor.ndim + assert tensor2.shape == tensor.shape + assert tensor.labels == tensor2.labels + assert new.shape != tensor.shape def test_concatenation_3D(): data_1 = torch.rand(20, 3, 4) @@ -146,3 +152,51 @@ def test_concatenation_3D(): assert lt_cat.labels[2]['dof'] == range(5) assert lt_cat.labels[0]['dof'] == range(20) assert lt_cat.labels[1]['dof'] == range(3) + + +def test_summation(): + lt1 = LabelTensor(torch.ones(20,3), labels_all) + lt2 = LabelTensor(torch.ones(30,3), ['x', 'y', 'z']) + with pytest.raises(RuntimeError): + LabelTensor.summation([lt1, lt2]) + lt1 = LabelTensor(torch.ones(20,3), labels_all) + lt2 = LabelTensor(torch.ones(20,3), labels_all) + lt_sum = LabelTensor.summation([lt1, lt2]) + assert lt_sum.ndim == lt_sum.ndim + assert lt_sum.shape[0] == 20 + assert lt_sum.shape[1] == 3 + assert lt_sum.labels == labels_all + assert torch.eq(lt_sum.tensor, torch.ones(20,3)*2).all() + lt1 = LabelTensor(torch.ones(20,3), labels_all) + lt2 = LabelTensor(torch.ones(20,3), labels_all) + lt3 = LabelTensor(torch.zeros(20, 3), labels_all) + lt_sum = LabelTensor.summation([lt1, lt2, lt3]) + assert lt_sum.ndim == lt_sum.ndim + assert lt_sum.shape[0] == 20 + assert lt_sum.shape[1] == 3 + assert lt_sum.labels == labels_all + assert torch.eq(lt_sum.tensor, torch.ones(20,3)*2).all() + +def test_append_3D(): + data_1 = torch.rand(20, 3, 4) + labels_1 = ['x', 'y', 'z', 'w'] + lt1 = LabelTensor(data_1, labels_1) + data_2 = torch.rand(50, 3, 4) + labels_2 = ['x', 'y', 'z', 'w'] + lt2 = LabelTensor(data_2, labels_2) + lt1 = lt1.append(lt2) + assert lt1.shape == (70, 3, 4) + assert lt1.labels[0]['dof'] == range(70) + assert lt1.labels[1]['dof'] == range(3) + assert lt1.labels[2]['dof'] == ['x', 'y', 'z', 'w'] + data_1 = torch.rand(20, 3, 2) + labels_1 = ['x', 'y'] + lt1 = LabelTensor(data_1, labels_1) + data_2 = torch.rand(20, 3, 2) + labels_2 = ['z', 'w'] + lt2 = LabelTensor(data_2, labels_2) + lt1 = lt1.append(lt2, mode='cross') + assert lt1.shape == (20, 3, 4) + assert lt1.labels[0]['dof'] == range(20) + assert lt1.labels[1]['dof'] == range(3) + assert lt1.labels[2]['dof'] == ['x', 'y', 'z', 'w'] diff --git a/tests/test_operators.py b/tests/test_operators.py index 58e90ca..e18eaf2 100644 --- a/tests/test_operators.py +++ b/tests/test_operators.py @@ -16,28 +16,29 @@ def func_scalar(x): return x_**2 + y_**2 + z_**2 -inp = LabelTensor(torch.rand((20, 3), requires_grad=True), ['x', 'y', 'z']) -tensor_v = LabelTensor(func_vector(inp), ['a', 'b', 'c']) -tensor_s = LabelTensor(func_scalar(inp).reshape(-1, 1), ['a']) +data = torch.rand((20, 3)) +inp = LabelTensor(data, ['x', 'y', 'mu']).requires_grad_(True) +labels = ['a', 'b', 'c'] +tensor_v = LabelTensor(func_vec(inp), labels) +tensor_s = LabelTensor(func_scalar(inp).reshape(-1, 1), labels[0]) + def test_grad_scalar_output(): grad_tensor_s = grad(tensor_s, inp) true_val = 2*inp assert grad_tensor_s.shape == inp.shape - assert grad_tensor_s.labels == [ - f'd{tensor_s.labels[0]}d{i}' for i in inp.labels + assert grad_tensor_s.labels[grad_tensor_s.ndim-1]['dof'] == [ + f'd{tensor_s.labels[tensor_s.ndim-1]["dof"][0]}d{i}' for i in inp.labels[inp.ndim-1]['dof'] ] assert torch.allclose(grad_tensor_s, true_val) grad_tensor_s = grad(tensor_s, inp, d=['x', 'y']) - true_val = 2*inp.extract(['x', 'y']) - assert grad_tensor_s.shape == (inp.shape[0], 2) - assert grad_tensor_s.labels == [ - f'd{tensor_s.labels[0]}d{i}' for i in ['x', 'y'] + assert grad_tensor_s.shape == (20, 2) + assert grad_tensor_s.labels[grad_tensor_s.ndim-1]['dof'] == [ + f'd{tensor_s.labels[tensor_s.ndim-1]["dof"][0]}d{i}' for i in ['x', 'y'] ] assert torch.allclose(grad_tensor_s, true_val) - def test_grad_vector_output(): grad_tensor_v = grad(tensor_v, inp) true_val = torch.cat( @@ -74,7 +75,6 @@ def test_grad_vector_output(): ] assert torch.allclose(grad_tensor_v, true_val) - def test_div_vector_output(): div_tensor_v = div(tensor_v, inp) true_val = 2*torch.sum(inp, dim=1).reshape(-1,1) @@ -88,7 +88,6 @@ def test_div_vector_output(): assert div_tensor_v.labels == [f'dadx+dbdy'] assert torch.allclose(div_tensor_v, true_val) - def test_laplacian_scalar_output(): laplace_tensor_s = laplacian(tensor_s, inp) true_val = 6*torch.ones_like(laplace_tensor_s) @@ -102,7 +101,6 @@ def test_laplacian_scalar_output(): assert laplace_tensor_s.labels == [f"dd{tensor_s.labels[0]}"] assert torch.allclose(laplace_tensor_s, true_val) - def test_laplacian_vector_output(): laplace_tensor_v = laplacian(tensor_v, inp) true_val = 2*torch.ones_like(tensor_v)