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base_main.py
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base_main.py
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#%%
import os
import warnings
warnings.filterwarnings("ignore")
import argparse
import clip
import numpy as np
import pickle
from termcolor import colored
import torch
import torch.nn.functional as F
from torch.utils.data import DataLoader
import torchmetrics
import tqdm
import waffle_tools
#%%
parser = argparse.ArgumentParser()
### Base arguments.
parser.add_argument('--mode', type=str, default='clip', choices=waffle_tools.METHODS,
help='VLM extension to use.')
parser.add_argument('--seed', type=int, default=1,
help='Replication seed.')
parser.add_argument('--batch_size', type=int, default=640,
help='Batchsize, mainly used to compute image embeddings.')
parser.add_argument('--dataset', type=str, default='imagenetv2', choices=waffle_tools.DATASETS,
help='Dataset to evaluate on.')
parser.add_argument('--model_size', type=str, default='ViT-B/32', choices=waffle_tools.BACKBONES,
help='Pretrained CLIP model to use.')
parser.add_argument('--aggregate', type=str, default='mean', choices=('mean', 'max'),
help='How to aggregate similarites of multiple language embeddings.')
### Text going before and after class names & descriptors.
### In the default case, this would be "A photo of a "<classname> ... <descriptors>"."
parser.add_argument('--label_before_text', type=str, default='A photo of a ',
help='Prompt-part going at the very beginning.')
parser.add_argument('--label_after_text', type=str, default='.',
help='Prompt-part going at the very end.')
###
parser.add_argument('--pre_descriptor_text', type=str, default='',
help='Text that goes right before the descriptor.')
parser.add_argument('--descriptor_separator', type=str, default=', ',
help='Text separating descriptor part and classname.')
###
parser.add_argument('--dont_apply_descriptor_modification', action='store_true',
help='Flag. If set, will not use "which (is/has/etc)" before descriptors.')
parser.add_argument('--merge_predictions', action='store_true',
help='Optional flag to merge generated embeddings before computing retrieval scores.')
parser.add_argument('--save_model', type=str, default='',
help='Set to a non-empty filename to store generated language embeddings & scores in a pickle file for all seed-repetitions.')
parser.add_argument('--randomization_budget', type=int, default=15,
help='Budget w.r.t. to DCLIP for randomization ablations')
parser.add_argument('--waffle_count', type=int, default=15,
help='For WaffleCLIP: Number of randomized descriptor pairs to use')
parser.add_argument('--reps', type=int, default=1,
help='Number of repetitions to run a method for with changing randomization. Default value should be >7 for WaffleCLIP variants.')
parser.add_argument('--savename', type=str, default='results',
help='Name of csv-file in which results are stored.')
###
parser.add_argument('--vmf_scale', type=float, default=1)
opt = parser.parse_args()
opt.apply_descriptor_modification = not opt.dont_apply_descriptor_modification
#%% Get dataloader and load model.
waffle_tools.seed_everything(opt.seed)
opt, dataset = waffle_tools.setup(opt)
print(colored(f"\nLoading model [{opt.model_size}] for dataset [{opt.dataset}] ...\n", "yellow", attrs=["bold"]))
opt.device = device = torch.device('cuda')
model, preprocess = clip.load(opt.model_size, device=device, jit=False)
model.eval()
model.requires_grad_(False)
#%% Compute image embeddings if not already precomputed.
precomputed_encs_folder = 'precomputed_encs'
os.makedirs(precomputed_encs_folder, exist_ok=True)
precomputed_encs_file = os.path.join(
precomputed_encs_folder,
f'{opt.dataset}_{opt.model_size.lower().replace("/", "")}.pkl'
)
if os.path.exists(precomputed_encs_file):
load_res = pickle.load(open(precomputed_encs_file, 'rb'))
else:
dataloader = DataLoader(dataset, opt.batch_size, shuffle=False, num_workers=8, pin_memory=True)
enc_coll = []
label_coll = []
with torch.no_grad():
for batch_number, batch in enumerate(tqdm.tqdm(dataloader, desc='Precomputing image embeddings...')):
images, labels = batch
images = images.to(device)
labels = labels.to(device)
label_coll.append(labels)
image_encodings = F.normalize(model.encode_image(images))
enc_coll.append(image_encodings.cpu())
load_res = {'enc': enc_coll, 'labels': label_coll}
pickle.dump(load_res, open(precomputed_encs_file, 'wb'))
encoding_coll = load_res['enc']
label_coll = load_res['labels']
#%% Generate Image Embeddings and compute scores.
accs1 = []
accs5 = []
scores_1 = []
scores_5 = []
encodings = []
for rep in range(opt.reps):
print(colored(f'----- Repetition {rep+1}/{opt.reps}', "green", attrs=["bold"]))
waffle_tools.seed_everything(rep)
accuracy_metric = torchmetrics.Accuracy().to(device)
accuracy_metric_top5 = torchmetrics.Accuracy(top_k=5).to(device)
description_encodings = waffle_tools.compute_description_encodings(opt, model, mode=opt.mode)
description_nums = [len(x) for x in description_encodings.values()]
print(f'Minimum and Maximum number of descriptions/class: {np.min(description_nums)} | {np.max(description_nums)}')
descr_means = torch.cat([x.mean(dim=0).reshape(1, -1) for x in description_encodings.values()])
descr_means /= descr_means.norm(dim=-1, keepdim=True)
for batch_number, (image_encodings, labels) in tqdm.tqdm(enumerate(zip(encoding_coll, label_coll)), total=len(encoding_coll), desc='Classifying image embeddings...'):
image_encodings = image_encodings.to(device)
labels = labels.to(device)
if opt.merge_predictions:
image_description_similarity = image_encodings @ descr_means.T
else:
image_description_similarity_t = [None] * opt.n_classes
image_description_similarity_cumulative = [None] * opt.n_classes
for i, (k, v) in enumerate(description_encodings.items()): # You can also vectorize this; it wasn't much faster for me
image_description_similarity_t[i] = image_encodings @ v.T
image_description_similarity_cumulative[i] = waffle_tools.aggregate_similarity(image_description_similarity_t[i], aggregation_method=opt.aggregate)
image_description_similarity = torch.stack(image_description_similarity_cumulative, dim=1)
acc = accuracy_metric(image_description_similarity.softmax(dim=-1), labels)
acc_top5 = accuracy_metric_top5(image_description_similarity.softmax(dim=-1), labels)
accuracy_logs = {}
accuracy_logs[f"[Mode = {opt.mode}] Top-1 Accuracy: "] = 100 * accuracy_metric.compute().item()
accuracy_logs[f"[Mode = {opt.mode}] Top-5 Accuracy: "] = 100 * accuracy_metric_top5.compute().item()
accs1.append(accuracy_logs[f"[Mode = {opt.mode}] Top-1 Accuracy: "])
accs5.append(accuracy_logs[f"[Mode = {opt.mode}] Top-5 Accuracy: "])
print("\n")
for key, value in accuracy_logs.items():
print(key, '{0:3.2f}'.format(value))
print("\n")
scores_1.append(accs1[-1])
scores_5.append(accs5[-1])
encodings.append(description_encodings)
### Print final results.
print(colored("\nFinal results", "red", attrs=["bold"]))
print(f'After {opt.reps} reps using mode = {opt.mode} with merge = {opt.merge_predictions}:')
print(colored("Top-1 Accuracy", "white", attrs=["bold"]))
print('Mean Top-1 Acc: {0:3.2f}% +- {1:3.2f}%'.format(np.mean(accs1), np.std(accs1)))
print('Min and Max Top-1 Acc: {0:3.2f}% | {1:3.2f}%'.format(np.min(accs1), np.max(accs1)))
print('All Top-1 Accs: {0}'.format(' | '.join('{0:3.2f}%'.format(x) for x in accs1)))
print(colored("Top-5 Accuracy", "white", attrs=["bold"]))
print('Mean Top-5 Acc: {0:3.2f}% +- {1:3.2f}%'.format(np.mean(accs5), np.std(accs5)))
print('Min and Max Top-5 Acc: {0:3.2f}% | {1:3.2f}%'.format(np.min(accs5), np.max(accs5)))
print('All Top-5 Accs: {0}'.format(' | '.join('{0:3.2f}%'.format(x) for x in accs5)))
### Save results as csv.
import sys
import csv
os.makedirs('results', exist_ok=True)
savename = '; '.join(x.replace('--','') for x in sys.argv[1:])
with open(f'results/{opt.savename}.csv', 'a') as csv_file:
writer = csv.writer(csv_file)
writer.writerow([savename, np.mean(accs1), np.std(accs1), np.max(accs1), np.min(accs1), np.mean(accs5), np.std(accs5), np.max(accs5), np.min(accs5)])
csv_file.close()
### Save model information as pkl.
if opt.save_model != '':
os.makedirs('stored_models', exist_ok=True)
pickle.dump({'scores_1': scores_1, 'scores_5': scores_5, 'encodings': encodings}, open(f'stored_models/{opt.save_model}_{opt.dataset}.pkl', 'wb'))