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evaluate_gen.py
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import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.utils.data import DataLoader
from dataloader.dataloader_visdial_disc import VisdialDataset
from dataloader.dataloader_visdial_gen import VisdialDataset
from models.visual_dialog_encoder import VisualDialogEncoder
from models.visual_dialog_decoder import VisualDialogDecoder
from models.visual_dialog_model import EncoderDecoderModel
from utils.logger import Logger
import options
import pprint
import os
import json
from train_gen import forward
from utils.visdial_metrics import SparseGTMetrics, NDCG, scores_to_ranks
from pytorch_transformers.tokenization_bert import BertTokenizer
from utils.data_utils import sequence_mask, batch_iter
def evaluate(model, dataloader, params, eval_batch_size, mode='vd_eval_val'):
sparse_metrics = SparseGTMetrics()
ndcg = NDCG()
ranks_json = []
model.eval()
batch_idx = 0
with torch.no_grad():
batch_size = 500
print("batch size for evaluation", batch_size)
for epoch_id, _, batch in batch_iter(dataloader, params):
if epoch_id == 1:
break
# language stuff
enc_input_ids = batch['enc_input_ids']
enc_segments = batch['enc_segments']
enc_sep_indices = batch['enc_sep_indices']
enc_mlm_labels = batch['enc_mlm_labels']
enc_hist_len = batch['enc_hist_len']
enc_att_mask = batch['enc_att_mask']
dec_input_ids = batch['dec_input_ids']
dec_att_mask = batch['dec_att_mask']
num_rounds = enc_input_ids.shape[1]
num_options = enc_input_ids.shape[2]
enc_input_ids = enc_input_ids.view(-1, enc_input_ids.shape[-1])
enc_segments = enc_segments.view(-1, enc_segments.shape[-1])
enc_sep_indices = enc_sep_indices.view(-1, enc_sep_indices.shape[-1])
enc_mlm_labels = enc_mlm_labels.view(-1, enc_mlm_labels.shape[-1])
enc_hist_len = enc_hist_len.view(-1)
enc_att_mask = enc_att_mask.view(-1, enc_att_mask.shape[-1])
dec_input_ids = dec_input_ids.view(-1,dec_input_ids.shape[-1])
dec_att_mask = dec_att_mask.view(-1, dec_att_mask.shape[-1])
# image stuff
enc_image_features = batch['enc_image_feat']
enc_image_spatials = batch['enc_image_loc']
enc_image_mask = batch['enc_image_mask']
# expand the image features to match those of tokens etc.
max_num_regions = enc_image_features.shape[-2]
enc_image_features = enc_image_features.unsqueeze(1).unsqueeze(1).expand(eval_batch_size, num_rounds, num_options, max_num_regions, 2048).contiguous()
enc_image_spatials = enc_image_spatials.unsqueeze(1).unsqueeze(1).expand(eval_batch_size, num_rounds, num_options, max_num_regions, 5).contiguous()
enc_image_mask = enc_image_mask.unsqueeze(1).unsqueeze(1).expand(eval_batch_size, num_rounds, num_options, max_num_regions).contiguous()
enc_image_features = enc_image_features.view(-1, max_num_regions, 2048)
enc_image_spatials = enc_image_spatials.view(-1, max_num_regions, 5)
enc_image_mask = enc_image_mask.view(-1, max_num_regions)
assert enc_input_ids.shape[0] == enc_segments.shape[0] == enc_sep_indices.shape[0] == enc_mlm_labels.shape[0] == \
enc_hist_len.shape[0] == enc_att_mask.shape[0] == dec_input_ids.shape[0] == dec_att_mask.shape[0] == \
enc_image_features.shape[0] == enc_image_spatials.shape[0] == enc_image_mask.shape[0] == num_rounds * num_options * eval_batch_size
output = []
assert (eval_batch_size * num_rounds * num_options)//batch_size == (eval_batch_size * num_rounds * num_options)/batch_size
for j in range((eval_batch_size * num_rounds * num_options)//batch_size):
# create chunks of the original batch
item = {}
item['enc_input_ids'] = enc_input_ids[j*batch_size:(j+1)*batch_size,:]
item['enc_segments'] = enc_segments[j*batch_size:(j+1)*batch_size,:]
item['enc_sep_indices'] = enc_sep_indices[j*batch_size:(j+1)*batch_size,:]
item['enc_mlm_labels'] = enc_mlm_labels[j*batch_size:(j+1)*batch_size,:]
item['enc_hist_len'] = enc_hist_len[j*batch_size:(j+1)*batch_size]
item['enc_att_mask'] = enc_att_mask[j*batch_size:(j+1)*batch_size,:]
item['dec_input_ids'] = dec_input_ids[j*batch_size:(j+1)*batch_size,:]
item['dec_att_mask'] = dec_att_mask[j*batch_size:(j+1)*batch_size,:]
item['enc_image_feat'] = enc_image_features[j*batch_size:(j+1)*batch_size, : , :]
item['enc_image_loc'] = enc_image_spatials[j*batch_size:(j+1)*batch_size, : , :]
item['enc_image_mask'] = enc_image_mask[j*batch_size:(j+1)*batch_size, :]
_, lm_scores = forward(model, item, params) # (batch_size, seq_len, vocab_size)
lm_scores = F.log_softmax(lm_scores, dim=-1)
# remove CLS tokens in the target answers
target_ans_ids = item['dec_input_ids'].to(params['device'])
shifted_target_ans_ids = target_ans_ids.new_zeros(target_ans_ids.shape)
shifted_target_ans_ids[:, :-1] = target_ans_ids[:, 1:].clone()
target_ans_ids = shifted_target_ans_ids
ans_scores = torch.gather(lm_scores, -1, target_ans_ids.unsqueeze(-1)).squeeze(-1)
# exclude zero-padded tokens when computing likelihood scores
ans_scores = ans_scores * (target_ans_ids != 0).float()
ans_scores = ans_scores.sum(-1)
output.append(ans_scores)
# print("output shape",torch.cat(output,0).shape)
output = torch.cat(output,0).view(eval_batch_size, num_rounds, num_options)
if mode == 'vd_eval_val':
gt_option_inds = batch['gt_option_inds']
sparse_metrics.observe(output, gt_option_inds)
if params['vd_version'] == "1.0":
gt_relevance = batch['gt_relevance']
gt_relevance_round_id = batch['round_id'].squeeze(1)
output = output[torch.arange(output.size(0)), gt_relevance_round_id - 1, :]
ndcg.observe(output, gt_relevance)
else:
ranks = scores_to_ranks(output)
ranks = ranks.squeeze(1)
for i in range(eval_batch_size):
ranks_json.append(
{
"image_id": batch["image_id"][i].item(),
"round_id": int(batch["round_id"][i].item()),
"ranks": [
rank.item()
for rank in ranks[i][:]
],
}
)
batch_idx += 1
if mode == 'vd_eval_val':
all_metrics = {}
all_metrics.update(sparse_metrics.retrieve(reset=True))
if params['vd_version'] == "1.0":
all_metrics.update(ndcg.retrieve(reset=True))
for metric_name, metric_value in all_metrics.items():
logger.write(f"{metric_name}: {metric_value}")
return ranks_json
if __name__ == '__main__':
params = options.read_command_line()
if not os.path.exists(params['save_path']):
os.makedirs(params['save_path'], exist_ok=True)
pprint.pprint(params)
dataset = VisdialDataset(params)
eval_batch_size = 20 if params['vd_version'] == '1.0' else 25
save_name = params['save_name'] if params['save_name'] else 'performance_log.txt'
logger = Logger(os.path.join(params['save_path'], save_name))
mode = params['mode']
assert mode == 'vd_eval_val' or mode == 'vd_eval_test'
dataset.mode = mode
params['model'] == 'enc_dec_a'
dataloader = DataLoader(
dataset,
batch_size=eval_batch_size,
shuffle=False,
num_workers=params['num_workers'],
drop_last=False,
pin_memory=False
)
device = (
torch.device("cuda", params["gpu_ids"][0])
if params["gpu_ids"][0] >= 0
else torch.device("cpu")
)
params['device'] = device
dialog_encoder = VisualDialogEncoder(params)
dialog_decoder = VisualDialogDecoder(params)
# share embedding layers
dialog_decoder.decoder.bert.embeddings = dialog_encoder.bert_pretrained.bert.embeddings
model = EncoderDecoderModel(params, dialog_encoder, dialog_decoder).to(device)
model = nn.DataParallel(model, params["gpu_ids"])
# load pretrained model
assert params['start_path'] != ''
model_state_dict = torch.load(params['start_path'], map_location=device)
model.module.load_state_dict(model_state_dict['model_state_dict'])
print("model succesfully loaded from {}".format(params['start_path']))
ranks_json = evaluate(model, dataloader, params, eval_batch_size, mode=mode)
if mode == 'vd_eval_test':
json.dump(ranks_json, open(os.path.join(parsed['save_path'], 'predictions.txt'), "w"))