from torch.functional import Tensor import torch import inspect import json import yaml import time import sys from general_utils import log import numpy as np from os.path import expanduser, join, isfile, realpath from torch.utils.data import DataLoader from metrics import FixedIntervalMetrics from general_utils import load_model, log, score_config_from_cli_args, AttributeDict, get_attribute, filter_args DATASET_CACHE = dict() def load_model(checkpoint_id, weights_file=None, strict=True, model_args='from_config', with_config=False, ignore_weights=False): config = json.load(open(join('logs', checkpoint_id, 'config.json'))) if model_args != 'from_config' and type(model_args) != dict: raise ValueError('model_args must either be "from_config" or a dictionary of values') model_cls = get_attribute(config['model']) # load model if model_args == 'from_config': _, model_args, _ = filter_args(config, inspect.signature(model_cls).parameters) model = model_cls(**model_args) if weights_file is None: weights_file = realpath(join('logs', checkpoint_id, 'weights.pth')) else: weights_file = realpath(join('logs', checkpoint_id, weights_file)) if isfile(weights_file) and not ignore_weights: weights = torch.load(weights_file) for _, w in weights.items(): assert not torch.any(torch.isnan(w)), 'weights contain NaNs' model.load_state_dict(weights, strict=strict) else: if not ignore_weights: raise FileNotFoundError(f'model checkpoint {weights_file} was not found') if with_config: return model, config return model def compute_shift2(model, datasets, seed=123, repetitions=1): """ computes shift """ model.eval() model.cuda() import random random.seed(seed) preds, gts = [], [] for i_dataset, dataset in enumerate(datasets): loader = DataLoader(dataset, batch_size=1, num_workers=0, shuffle=False, drop_last=False) max_iterations = int(repetitions * len(dataset.dataset.data_list)) with torch.no_grad(): i, losses = 0, [] for i_all, (data_x, data_y) in enumerate(loader): data_x = [v.cuda(non_blocking=True) if v is not None else v for v in data_x] data_y = [v.cuda(non_blocking=True) if v is not None else v for v in data_y] pred, = model(data_x[0], data_x[1], data_x[2]) preds += [pred.detach()] gts += [data_y] i += 1 if max_iterations and i >= max_iterations: break from metrics import FixedIntervalMetrics n_values = 51 thresholds = np.linspace(0, 1, n_values)[1:-1] metric = FixedIntervalMetrics(resize_pred=True, sigmoid=True, n_values=n_values) for p, y in zip(preds, gts): metric.add(p.unsqueeze(1), y) best_idx = np.argmax(metric.value()['fgiou_scores']) best_thresh = thresholds[best_idx] return best_thresh def get_cached_pascal_pfe(split, config): from datasets.pfe_dataset import PFEPascalWrapper try: dataset = DATASET_CACHE[(split, config.image_size, config.label_support, config.mask)] except KeyError: dataset = PFEPascalWrapper(mode='val', split=split, mask=config.mask, image_size=config.image_size, label_support=config.label_support) DATASET_CACHE[(split, config.image_size, config.label_support, config.mask)] = dataset return dataset def main(): config, train_checkpoint_id = score_config_from_cli_args() metrics = score(config, train_checkpoint_id, None) for dataset in metrics.keys(): for k in metrics[dataset]: if type(metrics[dataset][k]) in {float, int}: print(dataset, f'{k:<16} {metrics[dataset][k]:.3f}') def score(config, train_checkpoint_id, train_config): config = AttributeDict(config) print(config) # use training dataset and loss train_config = AttributeDict(json.load(open(f'logs/{train_checkpoint_id}/config.json'))) cp_str = f'_{config.iteration_cp}' if config.iteration_cp is not None else '' model_cls = get_attribute(train_config['model']) _, model_args, _ = filter_args(train_config, inspect.signature(model_cls).parameters) model_args = {**model_args, **{k: config[k] for k in ['process_cond', 'fix_shift'] if k in config}} strict_models = {'ConditionBase4', 'PFENetWrapper'} model = load_model(train_checkpoint_id, strict=model_cls.__name__ in strict_models, model_args=model_args, weights_file=f'weights{cp_str}.pth', ) model.eval() model.cuda() metric_args = dict() if 'threshold' in config: if config.metric.split('.')[-1] == 'SkLearnMetrics': metric_args['threshold'] = config.threshold if 'resize_to' in config: metric_args['resize_to'] = config.resize_to if 'sigmoid' in config: metric_args['sigmoid'] = config.sigmoid if 'custom_threshold' in config: metric_args['custom_threshold'] = config.custom_threshold if config.test_dataset == 'pascal': loss_fn = get_attribute(train_config.loss) # assume that if no split is specified in train_config, test on all splits, if 'splits' in config: splits = config.splits else: if 'split' in train_config and type(train_config.split) == int: # unless train_config has a split set, in that case assume train mode in training splits = [train_config.split] assert train_config.mode == 'train' else: splits = [0,1,2,3] log.info('Test on these splits', splits) scores = dict() for split in splits: shift = config.shift if 'shift' in config else 0 # automatic shift if shift == 'auto': shift_compute_t = time.time() shift = compute_shift2(model, [get_cached_pascal_pfe(s, config) for s in range(4) if s != split], repetitions=config.compute_shift_fac) log.info(f'Best threshold is {shift}, computed on splits: {[s for s in range(4) if s != split]}, took {time.time() - shift_compute_t:.1f}s') dataset = get_cached_pascal_pfe(split, config) eval_start_t = time.time() loader = DataLoader(dataset, batch_size=1, num_workers=0, shuffle=False, drop_last=False) assert config.batch_size is None or config.batch_size == 1, 'When PFE Dataset is used, batch size must be 1' metric = FixedIntervalMetrics(resize_pred=True, sigmoid=True, custom_threshold=shift, **metric_args) with torch.no_grad(): i, losses = 0, [] for i_all, (data_x, data_y) in enumerate(loader): data_x = [v.cuda(non_blocking=True) if isinstance(v, torch.Tensor) else v for v in data_x] data_y = [v.cuda(non_blocking=True) if isinstance(v, torch.Tensor) else v for v in data_y] if config.mask == 'separate': # for old CondBase model pred, = model(data_x[0], data_x[1], data_x[2]) else: # assert config.mask in {'text', 'highlight'} pred, _, _, _ = model(data_x[0], data_x[1], return_features=True) # loss = loss_fn(pred, data_y[0]) metric.add(pred.unsqueeze(1) + shift, data_y) # losses += [float(loss)] i += 1 if config.max_iterations and i >= config.max_iterations: break #scores[split] = {m: s for m, s in zip(metric.names(), metric.value())} log.info(f'Dataset length: {len(dataset)}, took {time.time() - eval_start_t:.1f}s to evaluate.') print(metric.value()['mean_iou_scores']) scores[split] = metric.scores() log.info(f'Completed split {split}') key_prefix = config['name'] if 'name' in config else 'pas' all_keys = set.intersection(*[set(v.keys()) for v in scores.values()]) valid_keys = [k for k in all_keys if all(v[k] is not None and isinstance(v[k], (int, float, np.float)) for v in scores.values())] return {key_prefix: {k: np.mean([s[k] for s in scores.values()]) for k in valid_keys}} if config.test_dataset == 'coco': from datasets.coco_wrapper import COCOWrapper coco_dataset = COCOWrapper('test', fold=train_config.fold, image_size=train_config.image_size, mask=config.mask, with_class_label=True) log.info('Dataset length', len(coco_dataset)) loader = DataLoader(coco_dataset, batch_size=config.batch_size, num_workers=2, shuffle=False, drop_last=False) metric = get_attribute(config.metric)(resize_pred=True, **metric_args) shift = config.shift if 'shift' in config else 0 with torch.no_grad(): i, losses = 0, [] for i_all, (data_x, data_y) in enumerate(loader): data_x = [v.cuda(non_blocking=True) if isinstance(v, torch.Tensor) else v for v in data_x] data_y = [v.cuda(non_blocking=True) if isinstance(v, torch.Tensor) else v for v in data_y] if config.mask == 'separate': # for old CondBase model pred, = model(data_x[0], data_x[1], data_x[2]) else: # assert config.mask in {'text', 'highlight'} pred, _, _, _ = model(data_x[0], data_x[1], return_features=True) metric.add([pred + shift], data_y) i += 1 if config.max_iterations and i >= config.max_iterations: break key_prefix = config['name'] if 'name' in config else 'coco' return {key_prefix: metric.scores()} #return {key_prefix: {k: v for k, v in zip(metric.names(), metric.value())}} if config.test_dataset == 'phrasecut': from datasets.phrasecut import PhraseCut only_visual = config.only_visual is not None and config.only_visual with_visual = config.with_visual is not None and config.with_visual dataset = PhraseCut('test', image_size=train_config.image_size, mask=config.mask, with_visual=with_visual, only_visual=only_visual, aug_crop=False, aug_color=False) loader = DataLoader(dataset, batch_size=config.batch_size, num_workers=2, shuffle=False, drop_last=False) metric = get_attribute(config.metric)(resize_pred=True, **metric_args) shift = config.shift if 'shift' in config else 0 with torch.no_grad(): i, losses = 0, [] for i_all, (data_x, data_y) in enumerate(loader): data_x = [v.cuda(non_blocking=True) if isinstance(v, torch.Tensor) else v for v in data_x] data_y = [v.cuda(non_blocking=True) if isinstance(v, torch.Tensor) else v for v in data_y] pred, _, _, _ = model(data_x[0], data_x[1], return_features=True) metric.add([pred + shift], data_y) i += 1 if config.max_iterations and i >= config.max_iterations: break key_prefix = config['name'] if 'name' in config else 'phrasecut' return {key_prefix: metric.scores()} #return {key_prefix: {k: v for k, v in zip(metric.names(), metric.value())}} if config.test_dataset == 'pascal_zs': from third_party.JoEm.model.metric import Evaluator from third_party.JoEm.data_loader import get_seen_idx, get_unseen_idx, VOC from datasets.pascal_zeroshot import PascalZeroShot, PASCAL_VOC_CLASSES_ZS from models.clipseg import CLIPSegMultiLabel n_unseen = train_config.remove_classes[1] pz = PascalZeroShot('val', n_unseen, image_size=352) m = CLIPSegMultiLabel(model=train_config.name).cuda() m.eval(); print(len(pz), n_unseen) print('training removed', [c for class_set in PASCAL_VOC_CLASSES_ZS[:n_unseen // 2] for c in class_set]) print('unseen', [VOC[i] for i in get_unseen_idx(n_unseen)]) print('seen', [VOC[i] for i in get_seen_idx(n_unseen)]) loader = DataLoader(pz, batch_size=8) evaluator = Evaluator(21, get_unseen_idx(n_unseen), get_seen_idx(n_unseen)) for i, (data_x, data_y) in enumerate(loader): pred = m(data_x[0].cuda()) evaluator.add_batch(data_y[0].numpy(), pred.argmax(1).cpu().detach().numpy()) if config.max_iter is not None and i > config.max_iter: break scores = evaluator.Mean_Intersection_over_Union() key_prefix = config['name'] if 'name' in config else 'pas_zs' return {key_prefix: {k: scores[k] for k in ['seen', 'unseen', 'harmonic', 'overall']}} elif config.test_dataset in {'same_as_training', 'affordance'}: loss_fn = get_attribute(train_config.loss) metric_cls = get_attribute(config.metric) metric = metric_cls(**metric_args) if config.test_dataset == 'same_as_training': dataset_cls = get_attribute(train_config.dataset) elif config.test_dataset == 'affordance': dataset_cls = get_attribute('datasets.lvis_oneshot3.LVIS_Affordance') dataset_name = 'aff' else: dataset_cls = get_attribute('datasets.lvis_oneshot3.LVIS_OneShot') dataset_name = 'lvis' _, dataset_args, _ = filter_args(config, inspect.signature(dataset_cls).parameters) dataset_args['image_size'] = train_config.image_size # explicitly use training image size for evaluation if model.__class__.__name__ == 'PFENetWrapper': dataset_args['image_size'] = config.image_size log.info('init dataset', str(dataset_cls)) dataset = dataset_cls(**dataset_args) log.info(f'Score on {model.__class__.__name__} on {dataset_cls.__name__}') data_loader = torch.utils.data.DataLoader(dataset, batch_size=config.batch_size, shuffle=config.shuffle) # explicitly set prompts if config.prompt == 'plain': model.prompt_list = ['{}'] elif config.prompt == 'fixed': model.prompt_list = ['a photo of a {}.'] elif config.prompt == 'shuffle': model.prompt_list = ['a photo of a {}.', 'a photograph of a {}.', 'an image of a {}.', '{}.'] elif config.prompt == 'shuffle_clip': from models.clip_prompts import imagenet_templates model.prompt_list = imagenet_templates config.assume_no_unused_keys(exceptions=['max_iterations']) t_start = time.time() with torch.no_grad(): # TODO: switch to inference_mode (torch 1.9) i, losses = 0, [] for data_x, data_y in data_loader: data_x = [x.cuda() if isinstance(x, torch.Tensor) else x for x in data_x] data_y = [x.cuda() if isinstance(x, torch.Tensor) else x for x in data_y] if model.__class__.__name__ in {'ConditionBase4', 'PFENetWrapper'}: pred, = model(data_x[0], data_x[1], data_x[2]) visual_q = None else: pred, visual_q, _, _ = model(data_x[0], data_x[1], return_features=True) loss = loss_fn(pred, data_y[0]) metric.add([pred], data_y) losses += [float(loss)] i += 1 if config.max_iterations and i >= config.max_iterations: break # scores = {m: s for m, s in zip(metric.names(), metric.value())} scores = metric.scores() keys = set(scores.keys()) if dataset.negative_prob > 0 and 'mIoU' in keys: keys.remove('mIoU') name_mask = dataset.mask.replace('text_label', 'txt')[:3] name_neg = '' if dataset.negative_prob == 0 else '_' + str(dataset.negative_prob) score_name = config.name if 'name' in config else f'{dataset_name}_{name_mask}{name_neg}' scores = {score_name: {k: v for k,v in scores.items() if k in keys}} scores[score_name].update({'test_loss': np.mean(losses)}) log.info(f'Evaluation took {time.time() - t_start:.1f}s') return scores else: raise ValueError('invalid test dataset') if __name__ == '__main__': main()