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import torch
import numpy as np
import random
import os
import time
import argparse
import logging
from open_clip import get_input_dtype
from training.distributed import is_master
from .metrics import compute_metrics, tensor_text_to_video_metrics, tensor_video_to_text_sim
# torch.distributed.init_process_group(backend="nccl")
from .util import parallel_apply
def _run_on_single_gpu(model,
# batch_list_t, batch_list_v,
batch_sequence_output_list, batch_visual_output_list):
sim_matrix = []
for idx1 in range(len(batch_sequence_output_list)):
# input_mask, segment_ids, *_tmp = b1
sequence_output = batch_sequence_output_list[idx1]
each_row = []
for idx2 in range(len(batch_visual_output_list)):
# video_mask, *_tmp = b2
visual_output = batch_visual_output_list[idx2]
# b1b2_logits, *_tmp = model.get_similarity_logits(sequence_output, visual_output, input_mask, video_mask,
# loose_type=model.loose_type)
# logging.info(f"{model.logit_scale.device}, {visual_output.device}, {sequence_output.device}")
b1b2_logits = model.logit_scale * sequence_output @ visual_output.T
# print(model.logit_scale.device, visual_output.device, sequence_output.device)
# logging.info(f"{b1b2_logits.shape}, {b1b2_logits.device}")
b1b2_logits = b1b2_logits.cpu().detach().numpy()
each_row.append(b1b2_logits)
each_row = np.concatenate(tuple(each_row), axis=-1)
sim_matrix.append(each_row)
return sim_matrix
def evaluate_vl_ret(model, data, epoch, args, tb_writer=None):
input_dtype = get_input_dtype(args.precision)
if is_master(args) and (args.val_frequency and ((epoch % args.val_frequency) == 0 or epoch == args.epochs)):
# print(data)
val_vl_ret_data = list(data.keys())
# print(val_vl_ret_data)
assert len(val_vl_ret_data) == 1
val_vl_ret_data = val_vl_ret_data[0]
test_dataloader = data[val_vl_ret_data]
device = model.device
n_gpu = torch.cuda.device_count()
logging.info(f"\nEval Epoch: {epoch}, eval Video-Text Retrieval under {val_vl_ret_data.upper()} test data")
if hasattr(model, 'module'):
model = model.module.to(device)
else:
model = model.to(device)
# #################################################################
## below variables are used to multi-sentences retrieval
# multi_sentence_: important tag for eval
# cut_off_points: used to tag the label when calculate the metric
# sentence_num: used to cut the sentence representation
# video_num: used to cut the video representation
# #################################################################
multi_sentence_ = False
cut_off_points_, sentence_num_, video_num_ = [], -1, -1
if hasattr(test_dataloader.dataset, 'multi_sentence_per_video') and test_dataloader.dataset.multi_sentence_per_video:
multi_sentence_ = True
cut_off_points_ = test_dataloader.dataset.cut_off_points
sentence_num_ = test_dataloader.dataset.sentence_num
video_num_ = test_dataloader.dataset.video_num
cut_off_points_ = [itm - 1 for itm in cut_off_points_]
if multi_sentence_:
logging.info("Eval under the multi-sentence per video clip setting.")
logging.info("sentence num: {}, video num: {}".format(sentence_num_, video_num_))
model.eval()
with torch.no_grad():
# batch_list_t = []
# batch_list_v = []
batch_sequence_output_list, batch_visual_output_list = [], []
total_video_num = 0
# ----------------------------
# 1. cache the features
# ----------------------------
for bid, batch in enumerate(test_dataloader):
# batch = tuple(t.to(device) for t in batch)
input_ids, attention_mask, _, video, _ = batch
# print(input_ids.shape, video.shape, video.dtype, end='-----')
input_ids = input_ids.squeeze().to(device)
attention_mask = attention_mask.squeeze().to(device)
# video = video.squeeze().permute(0, 2, 1, 3, 4).float().to(device)
video = video.float().to(device)
# video = video.to(ddevice=device, dtype=input_dtype)
# print(input_ids.shape, video.shape, video.dtype)
# print(input_ids.shape, video.shape)
if multi_sentence_:
# multi-sentences retrieval means: one clip has two or more descriptions.
b, *_t = video.shape
sequence_output = model.encode_text(input_ids, attention_mask)
# logging.info(f'multi: {sequence_output.shape}')
# sequence_output = model.get_sequence_output(input_ids, segment_ids, input_mask)
batch_sequence_output_list.append(sequence_output)
# batch_list_t.append((input_mask, segment_ids,))
s_, e_ = total_video_num, total_video_num + b
filter_inds = [itm - s_ for itm in cut_off_points_ if itm >= s_ and itm < e_]
if len(filter_inds) > 0:
# video, video_mask = video[filter_inds, ...], video_mask[filter_inds, ...]
# print('before', video.shape)
video = video[filter_inds, ...]
# print('after', video.shape)
# visual_output = model.get_visual_output(video, video_mask)
visual_output = model.encode_image(video)
batch_visual_output_list.append(visual_output)
# batch_list_v.append((video_mask,))
total_video_num += b
else:
sequence_output = model.encode_text(input_ids, attention_mask)
visual_output = model.encode_image(video)
# logging.info(f"{device}, {sequence_output.shape}, {visual_output.shape}")
# sequence_output, visual_output = model.get_sequence_visual_output(input_ids, segment_ids, input_mask, video, video_mask)
batch_sequence_output_list.append(sequence_output)
# batch_list_t.append((input_mask, segment_ids,))
batch_visual_output_list.append(visual_output)
# batch_list_v.append((video_mask,))
print(f"Process {val_vl_ret_data.upper()}: {bid}/{len(test_dataloader)}\r", end='')
# ----------------------------------
# 2. calculate the similarity
# ----------------------------------
n_gpu = torch.cuda.device_count()
if n_gpu > 1:
device_ids = list(range(n_gpu))
batch_t_output_splits = []
batch_v_output_splits = []
bacth_len = len(batch_sequence_output_list)
# print(bacth_len)
split_len = (bacth_len + n_gpu - 1) // n_gpu
for dev_id in device_ids:
s_, e_ = dev_id * split_len, (dev_id + 1) * split_len
if dev_id == 0:
batch_t_output_splits.append(batch_sequence_output_list[s_:e_])
batch_v_output_splits.append(batch_visual_output_list)
# print(len(batch_sequence_output_list[s_:e_]), len(batch_visual_output_list))
else:
devc = torch.device('cuda:{}'.format(str(dev_id)))
devc_batch_list = [b.to(devc) for b in batch_sequence_output_list[s_:e_]]
batch_t_output_splits.append(devc_batch_list)
devc_batch_list = [b.to(devc) for b in batch_visual_output_list]
batch_v_output_splits.append(devc_batch_list)
# print(len(devc_batch_list), len(devc_batch_list))
parameters_tuple_list = [(
batch_t_output_splits[dev_id], batch_v_output_splits[dev_id]) for dev_id in device_ids]
parallel_outputs = parallel_apply(_run_on_single_gpu, model, parameters_tuple_list, device_ids)
sim_matrix = []
for idx in range(len(parallel_outputs)):
sim_matrix += parallel_outputs[idx]
sim_matrix = np.concatenate(tuple(sim_matrix), axis=0)
else:
sim_matrix = _run_on_single_gpu(model,
# batch_list_t, batch_list_v,
batch_sequence_output_list, batch_visual_output_list)
sim_matrix = np.concatenate(tuple(sim_matrix), axis=0)
#####################################################################
if multi_sentence_:
logging.info(f"{val_vl_ret_data.upper()} before reshape, sim matrix size: {sim_matrix.shape}")
cut_off_points2len_ = [itm + 1 for itm in cut_off_points_]
max_length = max([e_-s_ for s_, e_ in zip([0]+cut_off_points2len_[:-1], cut_off_points2len_)])
sim_matrix_new = []
for s_, e_ in zip([0] + cut_off_points2len_[:-1], cut_off_points2len_):
sim_matrix_new.append(np.concatenate((sim_matrix[s_:e_],
np.full((max_length-e_+s_, sim_matrix.shape[1]), -np.inf)), axis=0))
sim_matrix = np.stack(tuple(sim_matrix_new), axis=0)
logging.info(f"{val_vl_ret_data.upper()} after reshape, sim matrix size: {sim_matrix.shape}")
tv_metrics = tensor_text_to_video_metrics(sim_matrix)
vt_metrics = compute_metrics(tensor_video_to_text_sim(sim_matrix))
else:
logging.info(f"{val_vl_ret_data.upper()} sim matrix size: {sim_matrix.shape[0]}, {sim_matrix.shape[1]}")
tv_metrics = compute_metrics(sim_matrix)
vt_metrics = compute_metrics(sim_matrix.T)
logging.info('\t Length-T: {}, Length-V:{}'.format(len(sim_matrix), len(sim_matrix[0])))
logging.info(f"{val_vl_ret_data.upper()} Text-to-Video:")
logging.info('\t>>> R@1: {:.1f} - R@5: {:.1f} - R@10: {:.1f} - Median R: {:.1f} - Mean R: {:.1f}'.
format(tv_metrics['R1'], tv_metrics['R5'], tv_metrics['R10'], tv_metrics['MR'], tv_metrics['MeanR']))
logging.info(f"{val_vl_ret_data.upper()} Video-to-Text:")
logging.info('\t>>> V2T$R@1: {:.1f} - V2T$R@5: {:.1f} - V2T$R@10: {:.1f} - V2T$Median R: {:.1f} - V2T$Mean R: {:.1f}'.
format(vt_metrics['R1'], vt_metrics['R5'], vt_metrics['R10'], vt_metrics['MR'], vt_metrics['MeanR']))
if args.save_logs:
for name, val in tv_metrics.items():
if tb_writer is not None:
tb_writer.add_scalar(f"val/vl_ret/{val_vl_ret_data}/t2v/{name}", val, epoch)
for name, val in vt_metrics.items():
if tb_writer is not None:
tb_writer.add_scalar(f"val/vl_ret/{val_vl_ret_data}/v2t/{name}", val, epoch)
args.vl_ret_output_dir = os.path.join(args.log_base_path, f'vl_ret/{val_vl_ret_data}')
os.makedirs(args.vl_ret_output_dir, exist_ok=True)
with open(os.path.join(args.vl_ret_output_dir, "results.jsonl"), "a+") as f:
f.write(json.dumps({'t2v': tv_metrics}))
f.write("\n")
f.write(json.dumps({'v2t': vt_metrics}))
f.write("\n")
# R1 = tv_metrics['R1']
# return R1
# torch.distributed.barrier() |