from .imports import * from .torch_imports import * from .core import * from .transforms import * from .model import * from .dataset import * from .sgdr import * from .layer_optimizer import * from .layers import * from .metrics import * from .losses import * from .swa import * from .fp16 import * from .lsuv_initializer import apply_lsuv_init import time class Learner(): def __init__(self, data, models, opt_fn=None, tmp_name='tmp', models_name='models', metrics=None, clip=None, crit=None): """ Combines a ModelData object with a nn.Module object, such that you can train that module. data (ModelData): An instance of ModelData. models(module): chosen neural architecture for solving a supported problem. opt_fn(function): optimizer function, uses SGD with Momentum of .9 if none. tmp_name(str): output name of the directory containing temporary files from training process models_name(str): output name of the directory containing the trained model metrics(list): array of functions for evaluating a desired metric. Eg. accuracy. clip(float): gradient clip chosen to limit the change in the gradient to prevent exploding gradients Eg. .3 """ self.data_,self.models,self.metrics = data,models,metrics self.sched=None self.wd_sched = None self.clip = None self.opt_fn = opt_fn or SGD_Momentum(0.9) self.tmp_path = tmp_name if os.path.isabs(tmp_name) else os.path.join(self.data.path, tmp_name) self.models_path = models_name if os.path.isabs(models_name) else os.path.join(self.data.path, models_name) os.makedirs(self.tmp_path, exist_ok=True) os.makedirs(self.models_path, exist_ok=True) self.crit = crit if crit else self._get_crit(data) self.reg_fn = None self.fp16 = False @classmethod def from_model_data(cls, m, data, **kwargs): self = cls(data, BasicModel(to_gpu(m)), **kwargs) self.unfreeze() return self def __getitem__(self,i): return self.children[i] @property def children(self): return children(self.model) @property def model(self): return self.models.model @property def data(self): return self.data_ def summary(self): return model_summary(self.model, [torch.rand(3, 3, self.data.sz,self.data.sz)]) def __repr__(self): return self.model.__repr__() def lsuv_init(self, needed_std=1.0, std_tol=0.1, max_attempts=10, do_orthonorm=False): x = V(next(iter(self.data.trn_dl))[0]) self.models.model=apply_lsuv_init(self.model, x, needed_std=needed_std, std_tol=std_tol, max_attempts=max_attempts, do_orthonorm=do_orthonorm, cuda=USE_GPU and torch.cuda.is_available()) def set_bn_freeze(self, m, do_freeze): if hasattr(m, 'running_mean'): m.bn_freeze = do_freeze def bn_freeze(self, do_freeze): apply_leaf(self.model, lambda m: self.set_bn_freeze(m, do_freeze)) def freeze_to(self, n): c=self.get_layer_groups() for l in c: set_trainable(l, False) for l in c[n:]: set_trainable(l, True) def freeze_all_but(self, n): c=self.get_layer_groups() for l in c: set_trainable(l, False) set_trainable(c[n], True) def freeze_groups(self, groups): c = self.get_layer_groups() self.unfreeze() for g in groups: set_trainable(c[g], False) def unfreeze_groups(self, groups): c = self.get_layer_groups() for g in groups: set_trainable(c[g], True) def unfreeze(self): self.freeze_to(0) def get_model_path(self, name): return os.path.join(self.models_path,name)+'.h5' def save(self, name): save_model(self.model, self.get_model_path(name)) if hasattr(self, 'swa_model'): save_model(self.swa_model, self.get_model_path(name)[:-3]+'-swa.h5') def load(self, name): load_model(self.model, self.get_model_path(name)) if hasattr(self, 'swa_model'): load_model(self.swa_model, self.get_model_path(name)[:-3]+'-swa.h5') def set_data(self, data): self.data_ = data def get_cycle_end(self, name): if name is None: return None return lambda sched, cycle: self.save_cycle(name, cycle) def save_cycle(self, name, cycle): self.save(f'{name}_cyc_{cycle}') def load_cycle(self, name, cycle): self.load(f'{name}_cyc_{cycle}') def half(self): if self.fp16: return self.fp16 = True if type(self.model) != FP16: self.models.model = FP16(self.model) def float(self): if not self.fp16: return self.fp16 = False if type(self.model) == FP16: self.models.model = self.model.module self.model.float() def fit_gen(self, model, data, layer_opt, n_cycle, cycle_len=None, cycle_mult=1, cycle_save_name=None, best_save_name=None, use_clr=None, use_clr_beta=None, metrics=None, callbacks=None, use_wd_sched=False, norm_wds=False, wds_sched_mult=None, use_swa=False, swa_start=1, swa_eval_freq=5, **kwargs): """Method does some preparation before finally delegating to the 'fit' method for fitting the model. Namely, if cycle_len is defined, it adds a 'Cosine Annealing' scheduler for varying the learning rate across iterations. Method also computes the total number of epochs to fit based on provided 'cycle_len', 'cycle_mult', and 'n_cycle' parameters. Args: model (Learner): Any neural architecture for solving a supported problem. Eg. ResNet-34, RNN_Learner etc. data (ModelData): An instance of ModelData. layer_opt (LayerOptimizer): An instance of the LayerOptimizer class n_cycle (int): number of cycles cycle_len (int): number of cycles before lr is reset to the initial value. E.g if cycle_len = 3, then the lr is varied between a maximum and minimum value over 3 epochs. cycle_mult (int): additional parameter for influencing how the lr resets over the cycles. For an intuitive explanation, please see https://github.com/fastai/fastai/blob/master/courses/dl1/lesson1.ipynb cycle_save_name (str): use to save the weights at end of each cycle best_save_name (str): use to save weights of best model during training. metrics (function): some function for evaluating a desired metric. Eg. accuracy. callbacks (list(Callback)): callbacks to apply during the training. use_wd_sched (bool, optional): set to True to enable weight regularization using the technique mentioned in https://arxiv.org/abs/1711.05101. When this is True alone (see below), the regularization is detached from gradient update and applied directly to the weights. norm_wds (bool, optional): when this is set to True along with use_wd_sched, the regularization factor is normalized with each training cycle. wds_sched_mult (function, optional): when this is provided along with use_wd_sched as True, the value computed by this function is multiplied with the regularization strength. This function is passed the WeightDecaySchedule object. And example function that can be passed is: f = lambda x: np.array(x.layer_opt.lrs) / x.init_lrs use_swa (bool, optional): when this is set to True, it will enable the use of Stochastic Weight Averaging (https://arxiv.org/abs/1803.05407). The learner will include an additional model (in the swa_model attribute) for keeping track of the average weights as described in the paper. All testing of this technique so far has been in image classification, so use in other contexts is not guaranteed to work. swa_start (int, optional): if use_swa is set to True, then this determines the epoch to start keeping track of the average weights. It is 1-indexed per the paper's conventions. swa_eval_freq (int, optional): if use_swa is set to True, this determines the frequency at which to evaluate the performance of the swa_model. This evaluation can be costly for models using BatchNorm (requiring a full pass through the data), which is why the default is not to evaluate after each epoch. Returns: None """ if callbacks is None: callbacks=[] if metrics is None: metrics=self.metrics if use_wd_sched: # This needs to come before CosAnneal() because we need to read the initial learning rate from # layer_opt.lrs - but CosAnneal() alters the layer_opt.lrs value initially (divides by 100) if np.sum(layer_opt.wds) == 0: print('fit() warning: use_wd_sched is set to True, but weight decay(s) passed are 0. Use wds to ' 'pass weight decay values.') batch_per_epoch = len(data.trn_dl) cl = cycle_len if cycle_len else 1 self.wd_sched = WeightDecaySchedule(layer_opt, batch_per_epoch, cl, cycle_mult, n_cycle, norm_wds, wds_sched_mult) callbacks += [self.wd_sched] if use_clr is not None: clr_div,cut_div = use_clr[:2] moms = use_clr[2:] if len(use_clr) > 2 else None cycle_end = self.get_cycle_end(cycle_save_name) self.sched = CircularLR(layer_opt, len(data.trn_dl)*cycle_len, on_cycle_end=cycle_end, div=clr_div, cut_div=cut_div, momentums=moms) elif use_clr_beta is not None: div,pct = use_clr_beta[:2] moms = use_clr_beta[2:] if len(use_clr_beta) > 3 else None cycle_end = self.get_cycle_end(cycle_save_name) self.sched = CircularLR_beta(layer_opt, len(data.trn_dl)*cycle_len, on_cycle_end=cycle_end, div=div, pct=pct, momentums=moms) elif cycle_len: cycle_end = self.get_cycle_end(cycle_save_name) cycle_batches = len(data.trn_dl)*cycle_len self.sched = CosAnneal(layer_opt, cycle_batches, on_cycle_end=cycle_end, cycle_mult=cycle_mult) elif not self.sched: self.sched=LossRecorder(layer_opt) callbacks+=[self.sched] if best_save_name is not None: callbacks+=[SaveBestModel(self, layer_opt, metrics, best_save_name)] if use_swa: # make a copy of the model to track average weights self.swa_model = copy.deepcopy(model) callbacks+=[SWA(model, self.swa_model, swa_start)] n_epoch = int(sum_geom(cycle_len if cycle_len else 1, cycle_mult, n_cycle)) return fit(model, data, n_epoch, layer_opt.opt, self.crit, metrics=metrics, callbacks=callbacks, reg_fn=self.reg_fn, clip=self.clip, fp16=self.fp16, swa_model=self.swa_model if use_swa else None, swa_start=swa_start, swa_eval_freq=swa_eval_freq, **kwargs) def get_layer_groups(self): return self.models.get_layer_groups() def get_layer_opt(self, lrs, wds): """Method returns an instance of the LayerOptimizer class, which allows for setting differential learning rates for different parts of the model. An example of how a model maybe differentiated into different parts for application of differential learning rates and weight decays is seen in ../.../courses/dl1/fastai/conv_learner.py, using the dict 'model_meta'. Currently, this seems supported only for convolutional networks such as VGG-19, ResNet-XX etc. Args: lrs (float or list(float)): learning rate(s) for the model wds (float or list(float)): weight decay parameter(s). Returns: An instance of a LayerOptimizer """ return LayerOptimizer(self.opt_fn, self.get_layer_groups(), lrs, wds) def fit(self, lrs, n_cycle, wds=None, **kwargs): """Method gets an instance of LayerOptimizer and delegates to self.fit_gen(..) Note that one can specify a list of learning rates which, when appropriately defined, will be applied to different segments of an architecture. This seems mostly relevant to ImageNet-trained models, where we want to alter the layers closest to the images by much smaller amounts. Likewise, a single or list of weight decay parameters can be specified, which if appropriate for a model, will apply variable weight decay parameters to different segments of the model. Args: lrs (float or list(float)): learning rate for the model n_cycle (int): number of cycles (or iterations) to fit the model for wds (float or list(float)): weight decay parameter(s). kwargs: other arguments Returns: None """ self.sched = None layer_opt = self.get_layer_opt(lrs, wds) return self.fit_gen(self.model, self.data, layer_opt, n_cycle, **kwargs) def warm_up(self, lr, wds=None): layer_opt = self.get_layer_opt(lr/4, wds) self.sched = LR_Finder(layer_opt, len(self.data.trn_dl), lr, linear=True) return self.fit_gen(self.model, self.data, layer_opt, 1) def lr_find(self, start_lr=1e-5, end_lr=10, wds=None, linear=False, **kwargs): """Helps you find an optimal learning rate for a model. It uses the technique developed in the 2015 paper `Cyclical Learning Rates for Training Neural Networks`, where we simply keep increasing the learning rate from a very small value, until the loss starts decreasing. Args: start_lr (float/numpy array) : Passing in a numpy array allows you to specify learning rates for a learner's layer_groups end_lr (float) : The maximum learning rate to try. wds (iterable/float) Examples: As training moves us closer to the optimal weights for a model, the optimal learning rate will be smaller. We can take advantage of that knowledge and provide lr_find() with a starting learning rate 1000x smaller than the model's current learning rate as such: >> learn.lr_find(lr/1000) >> lrs = np.array([ 1e-4, 1e-3, 1e-2 ]) >> learn.lr_find(lrs / 1000) Notes: lr_find() may finish before going through each batch of examples if the loss decreases enough. .. _Cyclical Learning Rates for Training Neural Networks: http://arxiv.org/abs/1506.01186 """ self.save('tmp') layer_opt = self.get_layer_opt(start_lr, wds) self.sched = LR_Finder(layer_opt, len(self.data.trn_dl), end_lr, linear=linear) self.fit_gen(self.model, self.data, layer_opt, 1, **kwargs) self.load('tmp') def lr_find2(self, start_lr=1e-5, end_lr=10, num_it = 100, wds=None, linear=False, stop_dv=True, **kwargs): """A variant of lr_find() that helps find the best learning rate. It doesn't do an epoch but a fixed num of iterations (which may be more or less than an epoch depending on your data). At each step, it computes the validation loss and the metrics on the next batch of the validation data, so it's slower than lr_find(). Args: start_lr (float/numpy array) : Passing in a numpy array allows you to specify learning rates for a learner's layer_groups end_lr (float) : The maximum learning rate to try. num_it : the number of iterations you want it to run wds (iterable/float) stop_dv : stops (or not) when the losses starts to explode. """ self.save('tmp') layer_opt = self.get_layer_opt(start_lr, wds) self.sched = LR_Finder2(layer_opt, num_it, end_lr, linear=linear, metrics=self.metrics, stop_dv=stop_dv) self.fit_gen(self.model, self.data, layer_opt, num_it//len(self.data.trn_dl) + 1, all_val=True, **kwargs) self.load('tmp') def predict(self, is_test=False, use_swa=False): dl = self.data.test_dl if is_test else self.data.val_dl m = self.swa_model if use_swa else self.model return predict(m, dl) def predict_with_targs(self, is_test=False, use_swa=False): dl = self.data.test_dl if is_test else self.data.val_dl m = self.swa_model if use_swa else self.model return predict_with_targs(m, dl) def predict_dl(self, dl): return predict_with_targs(self.model, dl)[0] def predict_array(self, arr): self.model.eval() return to_np(self.model(to_gpu(V(T(arr))))) def TTA(self, n_aug=4, is_test=False): """ Predict with Test Time Augmentation (TTA) Additional to the original test/validation images, apply image augmentation to them (just like for training images) and calculate the mean of predictions. The intent is to increase the accuracy of predictions by examining the images using multiple perspectives. Args: n_aug: a number of augmentation images to use per original image is_test: indicate to use test images; otherwise use validation images Returns: (tuple): a tuple containing: log predictions (numpy.ndarray): log predictions (i.e. `np.exp(log_preds)` will return probabilities) targs (numpy.ndarray): target values when `is_test==False`; zeros otherwise. """ dl1 = self.data.test_dl if is_test else self.data.val_dl dl2 = self.data.test_aug_dl if is_test else self.data.aug_dl preds1,targs = predict_with_targs(self.model, dl1) preds1 = [preds1]*math.ceil(n_aug/4) preds2 = [predict_with_targs(self.model, dl2)[0] for i in tqdm(range(n_aug), leave=False)] return np.stack(preds1+preds2), targs def fit_opt_sched(self, phases, cycle_save_name=None, best_save_name=None, stop_div=False, data_list=None, callbacks=None, cut = None, use_swa=False, swa_start=1, swa_eval_freq=5, **kwargs): """Wraps us the content of phases to send them to model.fit(..) This will split the training in several parts, each with their own learning rates/ wds/momentums/optimizer detailed in phases. Additionaly we can add a list of different data objets in data_list to train on different datasets (to change the size for instance) for each of these groups. Args: phases: a list of TrainingPhase objects stop_div: when True, stops the training if the loss goes too high data_list: a list of different Data objects. kwargs: other arguments use_swa (bool, optional): when this is set to True, it will enable the use of Stochastic Weight Averaging (https://arxiv.org/abs/1803.05407). The learner will include an additional model (in the swa_model attribute) for keeping track of the average weights as described in the paper. All testing of this technique so far has been in image classification, so use in other contexts is not guaranteed to work. swa_start (int, optional): if use_swa is set to True, then this determines the epoch to start keeping track of the average weights. It is 1-indexed per the paper's conventions. swa_eval_freq (int, optional): if use_swa is set to True, this determines the frequency at which to evaluate the performance of the swa_model. This evaluation can be costly for models using BatchNorm (requiring a full pass through the data), which is why the default is not to evaluate after each epoch. Returns: None """ if data_list is None: data_list=[] if callbacks is None: callbacks=[] layer_opt = LayerOptimizer(phases[0].opt_fn, self.get_layer_groups(), 1e-2, phases[0].wds) if len(data_list) == 0: nb_batches = [len(self.data.trn_dl)] * len(phases) else: nb_batches = [len(data.trn_dl) for data in data_list] self.sched = OptimScheduler(layer_opt, phases, nb_batches, stop_div) callbacks.append(self.sched) metrics = self.metrics if best_save_name is not None: callbacks+=[SaveBestModel(self, layer_opt, metrics, best_save_name)] if use_swa: # make a copy of the model to track average weights self.swa_model = copy.deepcopy(self.model) callbacks+=[SWA(self.model, self.swa_model, swa_start)] n_epochs = [phase.epochs for phase in phases] if cut is None else cut if len(data_list)==0: data_list = [self.data] return fit(self.model, data_list, n_epochs,layer_opt, self.crit, metrics=metrics, callbacks=callbacks, reg_fn=self.reg_fn, clip=self.clip, fp16=self.fp16, swa_model=self.swa_model if use_swa else None, swa_start=swa_start, swa_eval_freq=swa_eval_freq, **kwargs) def _get_crit(self, data): return F.mse_loss