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