From 7f52ef395178a3ab230993726d3df0a925906c20 Mon Sep 17 00:00:00 2001 From: lisab00 Date: Fri, 25 Aug 2023 14:23:19 +0200 Subject: [PATCH 01/11] update doc --- domainlab/models/model_diva.py | 34 +++++++++++++++++++++++++++++++++- 1 file changed, 33 insertions(+), 1 deletion(-) diff --git a/domainlab/models/model_diva.py b/domainlab/models/model_diva.py index 9d4b75beb..d03c0a6ab 100644 --- a/domainlab/models/model_diva.py +++ b/domainlab/models/model_diva.py @@ -10,8 +10,40 @@ def mk_diva(parent_class=VAEXYDClassif): """ - DIVA with arbitrary task loss + Instantiate a domain invariant variational autoencoder (DIVA) with arbitrary task loss. + + Details: + This method is creating a generative model based on a variational autoencoder, which can + reconstruct the input images. Here for, three different encoders with latent variables are + trained, each representing a latent subspace for the domain, class and residual features + information, respectively. The latent subspaces serve for disentangling the respective + sources of variation. To reconstruct the input image, the three latent variables are fed + into a decoder. + Additionally, two classifiers are trained, which predict the domain and the class label. + For more details, see: + Ilse, Maximilian, et al. "Diva: Domain invariant variational autoencoders." + Medical Imaging with Deep Learning. PMLR, 2020. + + Args: + parent_class: Class object determining the task type. Defaults to VAEXYDClassif. + + Returns: + ModelDIVA: model inheriting from parent class. + + Input Parameters: + zd_dim: size of latent space for domain-specific information + zy_dim: size of latent space for class-specific information + zx_dim: size of latent space for residual variance + chain_node_builder: TODO + list_str_y: list of labels + list_d_tr: list of training domains + gamma_d: weighting term for d classifier + gamma_y: weighting term for y classifier + beta_d: weighting term for domain encoder + beta_x: weighting term for residual variation encoder + beta_y: weighting term for class encoder """ + class ModelDIVA(parent_class): """ DIVA From e9ba2e4511eac6fba37d968285214179b5972d3b Mon Sep 17 00:00:00 2001 From: lisab00 Date: Mon, 28 Aug 2023 11:57:04 +0200 Subject: [PATCH 02/11] correct display of doc --- domainlab/models/model_diva.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/domainlab/models/model_diva.py b/domainlab/models/model_diva.py index d03c0a6ab..34f73c2c4 100644 --- a/domainlab/models/model_diva.py +++ b/domainlab/models/model_diva.py @@ -22,7 +22,7 @@ def mk_diva(parent_class=VAEXYDClassif): Additionally, two classifiers are trained, which predict the domain and the class label. For more details, see: Ilse, Maximilian, et al. "Diva: Domain invariant variational autoencoders." - Medical Imaging with Deep Learning. PMLR, 2020. + Medical Imaging with Deep Learning. PMLR, 2020. Args: parent_class: Class object determining the task type. Defaults to VAEXYDClassif. From 310b5b8b2d2cf6bd94d1b234459eca148bea75c8 Mon Sep 17 00:00:00 2001 From: lisab00 Date: Mon, 28 Aug 2023 12:04:46 +0200 Subject: [PATCH 03/11] correct display of doc --- domainlab/models/model_diva.py | 20 ++++++++++---------- 1 file changed, 10 insertions(+), 10 deletions(-) diff --git a/domainlab/models/model_diva.py b/domainlab/models/model_diva.py index 34f73c2c4..21da73fe0 100644 --- a/domainlab/models/model_diva.py +++ b/domainlab/models/model_diva.py @@ -31,16 +31,16 @@ def mk_diva(parent_class=VAEXYDClassif): ModelDIVA: model inheriting from parent class. Input Parameters: - zd_dim: size of latent space for domain-specific information - zy_dim: size of latent space for class-specific information - zx_dim: size of latent space for residual variance - chain_node_builder: TODO - list_str_y: list of labels - list_d_tr: list of training domains - gamma_d: weighting term for d classifier - gamma_y: weighting term for y classifier - beta_d: weighting term for domain encoder - beta_x: weighting term for residual variation encoder + zd_dim: size of latent space for domain-specific information, + zy_dim: size of latent space for class-specific information, + zx_dim: size of latent space for residual variance, + chain_node_builder: TODO, + list_str_y: list of labels, + list_d_tr: list of training domains, + gamma_d: weighting term for d classifier, + gamma_y: weighting term for y classifier, + beta_d: weighting term for domain encoder, + beta_x: weighting term for residual variation encoder, beta_y: weighting term for class encoder """ From 20884cb256a30737f061f711870dea00852c10c0 Mon Sep 17 00:00:00 2001 From: Car-la-F Date: Thu, 7 Sep 2023 12:20:39 +0200 Subject: [PATCH 04/11] bugfix --- domainlab/exp_protocol/benchmark.smk | 48 ++++++++++++-------- domainlab/utils/hyperparameter_gridsearch.py | 6 +-- 2 files changed, 30 insertions(+), 24 deletions(-) diff --git a/domainlab/exp_protocol/benchmark.smk b/domainlab/exp_protocol/benchmark.smk index fa55a7c29..e92e23d7e 100644 --- a/domainlab/exp_protocol/benchmark.smk +++ b/domainlab/exp_protocol/benchmark.smk @@ -2,6 +2,8 @@ import os import sys from pathlib import Path +import pandas as pd + try: config_path = workflow.configfiles[0] except IndexError: @@ -34,10 +36,13 @@ def experiment_result_files(_): else: num_nonsample_tasks += 1 # total number of hyperparameter samples - total_num_params = config['num_param_samples'] * num_sample_tasks + num_nonsample_tasks logger = Logger.get_logger() + hyperparam_df = pd.read_csv(f'{config["output_dir"]}/hyperparameters.csv') + total_num_params = hyperparam_df.shape[0] + #total_num_params = config['num_param_samples'] * num_sample_tasks + num_nonsample_tasks logger.info(f"total_num_params={total_num_params}") - logger.info(f"={config['num_param_samples']} * {num_sample_tasks} + {num_nonsample_tasks}") + #logger.info(f"={config['num_param_samples']} * {num_sample_tasks} + {num_nonsample_tasks}") + return [f"{config['output_dir']}/rule_results/{i}.csv" for i in range(total_num_params)] @@ -54,25 +59,29 @@ rule parameter_sampling: from domainlab.utils.hyperparameter_sampling import sample_hyperparameters from domainlab.utils.hyperparameter_gridsearch import sample_gridsearch - sampling_seed_str = params.sampling_seed - if isinstance(sampling_seed_str, str) and (len(sampling_seed_str) > 0): - # hash will keep integer intact and hash strings to random seed - # hased integer is signed and usually too big, random seed only - # allowed to be in [0, 2^32-1] - # if the user input is number, then hash will not change the value, - # so we recommend the user to use number as start seed - if sampling_seed_str.isdigit(): - sampling_seed = int(sampling_seed_str) - else: - sampling_seed = abs(hash(sampling_seed_str)) % (2 ** 32) - elif 'sampling_seed' in config.keys(): - sampling_seed = config['sampling_seed'] - else: - sampling_seed = None + # for gridsearch there is no random component, therefore no + # random seed is needed if 'mode' in config.keys(): # type(config)=dict if config['mode'] == 'grid': - sample_gridsearch(config,str(output.dest),sampling_seed) + sample_gridsearch(config,str(output.dest)) + # for random sampling we need to consider a random seed else: + sampling_seed_str = params.sampling_seed + if isinstance(sampling_seed_str, str) and (len(sampling_seed_str) > 0): + # hash will keep integer intact and hash strings to random seed + # hased integer is signed and usually too big, random seed only + # allowed to be in [0, 2^32-1] + # if the user input is number, then hash will not change the value, + # so we recommend the user to use number as start seed + if sampling_seed_str.isdigit(): + sampling_seed = int(sampling_seed_str) + else: + sampling_seed = abs(hash(sampling_seed_str)) % (2 ** 32) + elif 'sampling_seed' in config.keys(): + sampling_seed = config['sampling_seed'] + else: + sampling_seed = None + sample_hyperparameters(config, str(output.dest), sampling_seed) @@ -123,7 +132,8 @@ rule run_experiment: # in the resulting pandas dataframe # :param out_file: path to the output csv num_gpus = int(num_gpus_str) - run_experiment(config,str(input.param_file),index,str(output.out_file), start_seed, num_gpus=num_gpus) + run_experiment(config, str(input.param_file), index,str(output.out_file), + start_seed, num_gpus=num_gpus) rule agg_results: diff --git a/domainlab/utils/hyperparameter_gridsearch.py b/domainlab/utils/hyperparameter_gridsearch.py index ce05f7a40..aecd7a5fd 100644 --- a/domainlab/utils/hyperparameter_gridsearch.py +++ b/domainlab/utils/hyperparameter_gridsearch.py @@ -242,8 +242,7 @@ def grid_task(grid_df: pd.DataFrame, task_name: str, config: dict): def sample_gridsearch(config: dict, - dest: str = None, - sampling_seed: int = None) -> pd.DataFrame: + dest: str = None) -> pd.DataFrame: """ create the hyperparameters grid according to the given config, which should be the dictionary of the full @@ -257,9 +256,6 @@ def sample_gridsearch(config: dict, if dest is None: dest = config['output_dir'] + os.sep + 'hyperparameters.csv' - if not sampling_seed is None: - np.random.seed(sampling_seed) - logger = Logger.get_logger() samples = pd.DataFrame(columns=['task', 'algo', 'params']) for key, val in config.items(): From e386ab9e681a24da34659d9e7928ab1009006e64 Mon Sep 17 00:00:00 2001 From: Car-la-F Date: Thu, 7 Sep 2023 17:54:03 +0200 Subject: [PATCH 05/11] TODO: issue found in benchmark.smk line 40 --- domainlab/exp_protocol/benchmark.smk | 1 + 1 file changed, 1 insertion(+) diff --git a/domainlab/exp_protocol/benchmark.smk b/domainlab/exp_protocol/benchmark.smk index e92e23d7e..9fdf35457 100644 --- a/domainlab/exp_protocol/benchmark.smk +++ b/domainlab/exp_protocol/benchmark.smk @@ -37,6 +37,7 @@ def experiment_result_files(_): num_nonsample_tasks += 1 # total number of hyperparameter samples logger = Logger.get_logger() + #TODO this needs to be known before the hyperparameters are sampled hyperparam_df = pd.read_csv(f'{config["output_dir"]}/hyperparameters.csv') total_num_params = hyperparam_df.shape[0] #total_num_params = config['num_param_samples'] * num_sample_tasks + num_nonsample_tasks From 170377c9932920f26ed433157f0fad50701c7fa0 Mon Sep 17 00:00:00 2001 From: lisab00 Date: Mon, 11 Sep 2023 15:38:56 +0200 Subject: [PATCH 06/11] insert link, update doc --- domainlab/models/model_diva.py | 8 +++++++- 1 file changed, 7 insertions(+), 1 deletion(-) diff --git a/domainlab/models/model_diva.py b/domainlab/models/model_diva.py index 21da73fe0..94c2c1363 100644 --- a/domainlab/models/model_diva.py +++ b/domainlab/models/model_diva.py @@ -34,7 +34,9 @@ def mk_diva(parent_class=VAEXYDClassif): zd_dim: size of latent space for domain-specific information, zy_dim: size of latent space for class-specific information, zx_dim: size of latent space for residual variance, - chain_node_builder: TODO, + chain_node_builder: creates the neural network specified by the user; object of the class + "VAEChainNodeGetter" (see domainlab/compos/vae/utils_request_chain_builder.py) + being initialized by entering a user request, list_str_y: list of labels, list_d_tr: list of training domains, gamma_d: weighting term for d classifier, @@ -42,6 +44,10 @@ def mk_diva(parent_class=VAEXYDClassif): beta_d: weighting term for domain encoder, beta_x: weighting term for residual variation encoder, beta_y: weighting term for class encoder + + Usage: + For a concrete example, see: + https://github.com/marrlab/DomainLab/blob/master/tests/test_mk_exp_diva.py """ class ModelDIVA(parent_class): From de199691d485fea6fd0907892608ad86793075d7 Mon Sep 17 00:00:00 2001 From: smilesun Date: Tue, 12 Sep 2023 11:59:36 +0200 Subject: [PATCH 07/11] fstring, default debug --- domainlab/algos/msels/c_msel_val.py | 2 +- domainlab/arg_parser.py | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/domainlab/algos/msels/c_msel_val.py b/domainlab/algos/msels/c_msel_val.py index 3b7e3fad4..511bc3362 100644 --- a/domainlab/algos/msels/c_msel_val.py +++ b/domainlab/algos/msels/c_msel_val.py @@ -30,7 +30,7 @@ def update(self): else: self.es_c += 1 logger = Logger.get_logger() - logger.debug("early stop counter: ", self.es_c) + logger.debug(f"early stop counter: {self.es_c}") logger.debug(f"val acc:{self.tr_obs.metric_te['acc']}, " f"best validation acc: {self.best_val_acc}") flag = False # do not update best model diff --git a/domainlab/arg_parser.py b/domainlab/arg_parser.py index e0b73c792..53deb148a 100644 --- a/domainlab/arg_parser.py +++ b/domainlab/arg_parser.py @@ -178,7 +178,7 @@ def mk_parser_main(): arg_group_task.add_argument('--san_num', type=int, default=8, help='number of images to be dumped for the sanity check') - arg_group_task.add_argument('--loglevel', type=str, default='INFO', + arg_group_task.add_argument('--loglevel', type=str, default='DEBUG', help='sets the loglevel of the logger') # args for variational auto encoder From 3f95cd5b0dc97b2fc16ecbbd85912971564abc3d Mon Sep 17 00:00:00 2001 From: lisab00 Date: Tue, 12 Sep 2023 12:16:27 +0200 Subject: [PATCH 08/11] first draft of docFishr.md --- docs/docFishr.md | 73 ++++++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 73 insertions(+) create mode 100644 docs/docFishr.md diff --git a/docs/docFishr.md b/docs/docFishr.md new file mode 100644 index 000000000..9d3e629dc --- /dev/null +++ b/docs/docFishr.md @@ -0,0 +1,73 @@ +# Fishr: Invariant Gradient Variances for Out-of-distribution Generalization + +The goal of the Fishr regularization technique is locally aligning the domain-level loss landscapes +around the final weights, finding a minimizer around which the inconsistencies between +the domain-level loss landscapes is as small as possible. +This is done by considering second order terms during training, matching +the variances between the domain-level gradients. + + + + +### Measuring inconsistency between domains +Intuitively, two domains are locally inconsistent around a minimizer, if a small +perturbation of the minimizer highly affects its optimality in one domain, but only +minimally affects its optimality in the other domain. An exemplary visualization can +be found in figure XXXXX. Under certain assumptions, most importantly the Hessians being +positive definite, it is possible to measure the inconsistency between two domains +with the following inconsistency score: + +$$ +\mathcal{I}^\epsilon (\theta^*) = \textnormal{max}_{(A,B)\in\mathcal{E}^2} \biggl( \mathcal{R}_B(\theta^*) - \mathcal{R}_A(\theta^*) + \textnormal{max}_{\frac{1}{2}\theta^T H_A \theta\leq\epsilon}\frac{1}{2}\theta^T H_B \theta \biggl) +$$ + +, whereby $$\theta^*$$ denotes the minimizer, $$\mathcal{E}$$ denotes the set of training domains, +$$H_e$$ denotes the Hessian for $$e\in\mathcal{E}$$, §§\theta$$ denote the network parameters +and $$\mathcal{R}_e$$ for $$e\in\mathcal{E}$$ denotes the domain-level ERM objective. +The Fishr regularization method forces both terms in the right hand side +of the inconsistency score to become small. The first term represents the difference +between the domain-level risks and is implicitly forced to be small by applying +the Fishr regularization. For the second term it suffices to align diagonal approximations of the +domain-level Hessians, matching the variances across domains. + + + + +### Matching the Variances during training +Let $$\mathcal{E}$$ be the space of all training domains, and let $$\mathcal{R}_e(\theta) be the ERM +objective. Fishr minimizes the following objective function during training: + +$$ +\mathcal{L}(\theta) = \frac{1}{|\mathcal{E}|}\sum_{e\in\mathcal{E}} \mathcal{R}_\mathcal{E}(\theta) + \lambda \mathcal{L}_{\textnormal{Fishr}}(\theta) +$$ + +, whereby + +$$ +\mathcal{L}_\textnormal{Fishr}(\theta) = \frac{1}{|\mathcal{E}|}\sum_{e\in\mathcal{E}} \| v_e -v \|^2_2 +$$ + +with $$v_e$$ denoting the variance between the gradients of domain $$e\in\mathcal{E}$$ and +$$v$$ denoting the average variance of the gradients across all domains, i.e. +$$ Date: Wed, 13 Sep 2023 09:22:05 +0200 Subject: [PATCH 09/11] . --- docs/docFishr.md | 25 ++++++++++++++++--------- docs/figs/fishr.png | Bin 0 -> 176858 bytes 2 files changed, 16 insertions(+), 9 deletions(-) create mode 100644 docs/figs/fishr.png diff --git a/docs/docFishr.md b/docs/docFishr.md index 9d3e629dc..3d88cef07 100644 --- a/docs/docFishr.md +++ b/docs/docFishr.md @@ -2,20 +2,25 @@ The goal of the Fishr regularization technique is locally aligning the domain-level loss landscapes around the final weights, finding a minimizer around which the inconsistencies between -the domain-level loss landscapes is as small as possible. +the domain-level loss landscapes are as small as possible. This is done by considering second order terms during training, matching the variances between the domain-level gradients. +
+ +
Figure 1: Fishr matches the domain-level gradient variances of the +distributions across the training domains (Image source: Figure 1 of "Fishr: +Invariant gradient variances for out-of-distribution generalization")
+
-### Measuring inconsistency between domains +### Quantifying inconsistency between domains Intuitively, two domains are locally inconsistent around a minimizer, if a small perturbation of the minimizer highly affects its optimality in one domain, but only -minimally affects its optimality in the other domain. An exemplary visualization can -be found in figure XXXXX. Under certain assumptions, most importantly the Hessians being -positive definite, it is possible to measure the inconsistency between two domains -with the following inconsistency score: +minimally affects its optimality in the other domain. Under certain assumptions, most importantly +the Hessians being positive definite, it is possible to measure the inconsistency between two domains +$$A$$ and $$B$$ with the following inconsistency score: $$ \mathcal{I}^\epsilon (\theta^*) = \textnormal{max}_{(A,B)\in\mathcal{E}^2} \biggl( \mathcal{R}_B(\theta^*) - \mathcal{R}_A(\theta^*) + \textnormal{max}_{\frac{1}{2}\theta^T H_A \theta\leq\epsilon}\frac{1}{2}\theta^T H_B \theta \biggl) @@ -24,7 +29,7 @@ $$ , whereby $$\theta^*$$ denotes the minimizer, $$\mathcal{E}$$ denotes the set of training domains, $$H_e$$ denotes the Hessian for $$e\in\mathcal{E}$$, §§\theta$$ denote the network parameters and $$\mathcal{R}_e$$ for $$e\in\mathcal{E}$$ denotes the domain-level ERM objective. -The Fishr regularization method forces both terms in the right hand side +The Fishr regularization method forces both terms on the right hand side of the inconsistency score to become small. The first term represents the difference between the domain-level risks and is implicitly forced to be small by applying the Fishr regularization. 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7ef1acd332952e989ad7f20316f3f9a716c948fb Mon Sep 17 00:00:00 2001 From: Car-la-F Date: Thu, 14 Sep 2023 09:31:04 +0200 Subject: [PATCH 10/11] count grid samples for grid task and compute andm samples for random sampling task --- domainlab/exp_protocol/benchmark.smk | 45 +++++++++++++++++----------- 1 file changed, 28 insertions(+), 17 deletions(-) diff --git a/domainlab/exp_protocol/benchmark.smk b/domainlab/exp_protocol/benchmark.smk index 9fdf35457..ecb8b99a3 100644 --- a/domainlab/exp_protocol/benchmark.smk +++ b/domainlab/exp_protocol/benchmark.smk @@ -1,9 +1,9 @@ import os import sys from pathlib import Path - import pandas as pd + try: config_path = workflow.configfiles[0] except IndexError: @@ -26,23 +26,34 @@ def experiment_result_files(_): """Lists all expected i.csv""" from domainlab.utils.hyperparameter_sampling import is_dict_with_key from domainlab.utils.logger import Logger - # count tasks - num_sample_tasks = 0 - num_nonsample_tasks = 0 - for key, val in config.items(): - if is_dict_with_key(val, "aname"): - if 'hyperparameters' in val.keys(): - num_sample_tasks += 1 - else: - num_nonsample_tasks += 1 - # total number of hyperparameter samples + from domainlab.utils.hyperparameter_gridsearch import \ + sample_gridsearch + logger = Logger.get_logger() - #TODO this needs to be known before the hyperparameters are sampled - hyperparam_df = pd.read_csv(f'{config["output_dir"]}/hyperparameters.csv') - total_num_params = hyperparam_df.shape[0] - #total_num_params = config['num_param_samples'] * num_sample_tasks + num_nonsample_tasks - logger.info(f"total_num_params={total_num_params}") - #logger.info(f"={config['num_param_samples']} * {num_sample_tasks} + {num_nonsample_tasks}") + if config['mode'] == 'grid': + # hyperparameters are sampled using gridsearch + # in this case we don't know how many samples we will get beforehand + # straigt oreward solution: do a grid sampling and count samples + samples = sample_gridsearch(config) + total_num_params = samples.shape[0] + logger.info(f"total_num_params={total_num_params} for gridsearch") + else: + # in case of random sampling it is possible to compute the number + # of samples from the information in the yaml file + + # count tasks + num_sample_tasks = 0 + num_nonsample_tasks = 0 + for key, val in config.items(): + if is_dict_with_key(val, "aname"): + if 'hyperparameters' in val.keys(): + num_sample_tasks += 1 + else: + num_nonsample_tasks += 1 + # total number of hyperparameter samples + total_num_params = config['num_param_samples'] * num_sample_tasks + num_nonsample_tasks + logger.info(f"total_num_params={total_num_params} for random sampling") + logger.info(f"={config['num_param_samples']} * {num_sample_tasks} + {num_nonsample_tasks}") return [f"{config['output_dir']}/rule_results/{i}.csv" for i in range(total_num_params)] From c2773351a6fd07e983b84f8c2dba96de06472ea4 Mon Sep 17 00:00:00 2001 From: Car-la-F Date: Thu, 14 Sep 2023 10:01:36 +0200 Subject: [PATCH 11/11] add shared option to random search --- domainlab/exp_protocol/benchmark.smk | 5 ++++- 1 file changed, 4 insertions(+), 1 deletion(-) diff --git a/domainlab/exp_protocol/benchmark.smk b/domainlab/exp_protocol/benchmark.smk index ecb8b99a3..2e62bc69f 100644 --- a/domainlab/exp_protocol/benchmark.smk +++ b/domainlab/exp_protocol/benchmark.smk @@ -49,7 +49,10 @@ def experiment_result_files(_): if 'hyperparameters' in val.keys(): num_sample_tasks += 1 else: - num_nonsample_tasks += 1 + if 'shared' in val.keys(): + num_sample_tasks += 1 + else: + num_nonsample_tasks += 1 # total number of hyperparameter samples total_num_params = config['num_param_samples'] * num_sample_tasks + num_nonsample_tasks logger.info(f"total_num_params={total_num_params} for random sampling")