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train.py
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train.py
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import argparse
import itertools
from tensorboardX import SummaryWriter
import torch
from torch import nn, optim
from torch.optim import lr_scheduler
from torch.utils.data import DataLoader
from tqdm import tqdm
import yaml
from bisect import bisect
from numpy import random
from visdialch.data.dataset import VisDialDataset
from visdialch.encoders import Encoder
from visdialch.decoders import Decoder
from visdialch.metrics import SparseGTMetrics, NDCG, scores_to_ranks
from visdialch.model import EncoderDecoderModel
from visdialch.utils.checkpointing import CheckpointManager, load_checkpoint
from visdialch.data.vocabulary import Vocabulary
import json
parser = argparse.ArgumentParser()
parser.add_argument(
"--config-yml", default="configs/lf_disc_faster_rcnn_x101.yml",
help="Path to a config file listing reader, model and solver parameters."
)
parser.add_argument(
"--train-json", default="data/visdial_1.0_train.json",
help="Path to json file containing VisDial v1.0 training data."
)
parser.add_argument(
"--val-json", default="data/visdial_1.0_val.json",
help="Path to json file containing VisDial v1.0 validation data."
)
parser.add_argument(
"--val-dense-json", default="data/visdial_1.0_val_dense_annotations.json",
help="Path to json file containing VisDial v1.0 validation dense ground truth annotations."
)
parser.add_argument(
"--captions-train-json", default="data/train2018.json",
help="Path to json file containing VisDial v1.0 training captions data."
)
parser.add_argument(
"--captions-val-json", default="data/val2018.json",
help="Path to json file containing VisDial v1.0 validation captions data."
)
parser.add_argument_group("Arguments independent of experiment reproducibility")
parser.add_argument(
"--gpu-ids", nargs="+", type=int, default=0,
help="List of ids of GPUs to use."
)
parser.add_argument(
"--cpu-workers", type=int, default=4,
help="Number of CPU workers for dataloader."
)
parser.add_argument(
"--overfit", action="store_true",
help="Overfit model on 5 examples, meant for debugging."
)
parser.add_argument(
"--validate", action="store_true",
help="Whether to validate on val split after every epoch."
)
parser.add_argument(
"--in-memory", action="store_true",
help="Load the whole dataset and pre-extracted image features in memory. Use only in "
"presence of large RAM, atleast few tens of GBs."
)
parser.add_argument_group("Checkpointing related arguments")
parser.add_argument(
"--save-dirpath", default="checkpoints/",
help="Path of directory to create checkpoint directory and save checkpoints."
)
parser.add_argument(
"--load-pthpath", default="",
help="To continue training, path to .pth file of saved checkpoint."
)
# for reproducibility - refer https://pytorch.org/docs/stable/notes/randomness.html
torch.manual_seed(0)
torch.cuda.manual_seed_all(0)
torch.backends.cudnn.benchmark = False
torch.backends.cudnn.deterministic = True
# ================================================================================================
# INPUT ARGUMENTS AND CONFIG
# ================================================================================================
args = parser.parse_args()
# keys: {"dataset", "model", "solver"}
config = yaml.load(open(args.config_yml))
if isinstance(args.gpu_ids, int): args.gpu_ids = [args.gpu_ids]
device = torch.device("cuda", args.gpu_ids[0]) if args.gpu_ids[0] >= 0 else torch.device("cpu")
# Print config and args.
print(yaml.dump(config, default_flow_style=False))
for arg in vars(args):
print("{:<20}: {}".format(arg, getattr(args, arg)))
# ================================================================================================
# SETUP DATASET, DATALOADER, MODEL, CRITERION, OPTIMIZER, SCHEDULER
# ================================================================================================
train_dataset = VisDialDataset(
config["dataset"], args.train_json, args.captions_train_json, overfit=args.overfit, in_memory=args.in_memory
)
train_dataloader = DataLoader(
train_dataset, batch_size=config["solver"]["batch_size"], num_workers=args.cpu_workers, shuffle=True
)
val_dataset = VisDialDataset(
config["dataset"], args.val_json, args.captions_val_json, args.val_dense_json, overfit=args.overfit,
in_memory=args.in_memory
)
val_dataloader = DataLoader(
val_dataset, batch_size=config["solver"]["batch_size"], num_workers=args.cpu_workers
)
# Read GloVe word embedding data
with open(config["dataset"]["glovepath"], "r") as glove_file:
glove = json.load(glove_file)
glovevocabulary = Vocabulary(
config["dataset"]["word_counts_json"], min_count=config["dataset"]["vocab_min_count"]
)
KAT = []
for key in glove.keys():
keylist = [key]
token = glovevocabulary.to_indices(keylist)
key_and_token = keylist + token
KAT.append(key_and_token)
glove_token = {}
for item in KAT:
glove_token[item[1]] = glove[item[0]]
glove_list = []
for i in range(len(glovevocabulary)):
if i in glove_token.keys():
glove_list.append(glove_token[i])
else:
randArray = random.random(size=(1, 300)).tolist()
glove_list.append(randArray[0])
glove_token = torch.Tensor(glove_list).view(len(glovevocabulary), -1)
# Read ELMo word embedding data
with open(config["dataset"]["elmopath"], "r") as elmo_file:
elmo = json.load(elmo_file)
KAT = []
for key in elmo.keys():
keylist = [key]
token = glovevocabulary.to_indices(keylist)
key_and_token = keylist + token
KAT.append(key_and_token)
elmo_token = {}
for item in KAT:
elmo_token[item[1]] = elmo[item[0]]
elmo_list = []
for i in range(len(glovevocabulary)):
if i in elmo_token.keys():
elmo_list.append(elmo_token[i])
else:
randArray = random.random(size=(1, 1024)).tolist()
elmo_list.append(randArray[0])
elmo_token = torch.Tensor(elmo_list).view(len(glovevocabulary), -1)
# Pass vocabulary to construct Embedding layer.
encoder = Encoder(config["model"], train_dataset.vocabulary, glove_token, elmo_token)
decoder = Decoder(config["model"], train_dataset.vocabulary, glove_token, elmo_token)
print("Encoder: {}".format(config["model"]["encoder"]))
print("Decoder: {}".format(config["model"]["decoder"]))
# Share word embedding between encoder and decoder.
# decoder.word_embed = encoder.word_embed
decoder.glove_embed = encoder.glove_embed
decoder.elmo_embed = encoder.elmo_embed
decoder.embed_change = encoder.embed_change
# Wrap encoder and decoder in a model.
model = EncoderDecoderModel(encoder, decoder).to(device)
if -1 not in args.gpu_ids:
model = nn.DataParallel(model, args.gpu_ids)
# Loss function.
criterion = nn.CrossEntropyLoss()
if config["solver"]["training_splits"] == "trainval":
iterations = (len(train_dataset) + len(val_dataset)) // config["solver"]["batch_size"] + 1
else:
iterations = len(train_dataset) // config["solver"]["batch_size"] + 1
# lr_scheduler 1
def lr_lambda_fun(current_iteration: int) -> float:
current_epoch = float(current_iteration) / iterations
if current_epoch <= config["solver"]["warmup_epochs"]:
alpha = current_epoch / float(config["solver"]["warmup_epochs"])
return config["solver"]["warmup_factor"] * (1. - alpha) + alpha
else:
idx = bisect(config["solver"]["lr_milestones"], current_epoch)
return pow(config["solver"]["lr_gamma"], idx)
optimizer = optim.Adamax(model.parameters(), lr=config["solver"]["initial_lr"])
scheduler = lr_scheduler.LambdaLR(optimizer, lr_lambda=lr_lambda_fun)
T = iterations * (config["solver"]["num_epochs"] - config["solver"]["warmup_epochs"] + 1)
# lr_scheduler 2
scheduler2 = lr_scheduler.CosineAnnealingLR(optimizer, int(T), eta_min=config["solver"]["eta_min"], last_epoch=-1)
# ================================================================================================
# SETUP BEFORE TRAINING LOOP
# ================================================================================================
summary_writer = SummaryWriter(log_dir=args.save_dirpath)
checkpoint_manager = CheckpointManager(model, optimizer, args.save_dirpath, config=config)
sparse_metrics = SparseGTMetrics()
ndcg = NDCG()
# If loading from checkpoint, adjust start epoch and load parameters.
if args.load_pthpath == "":
start_epoch = 0
else:
# "path/to/checkpoint_xx.pth" -> xx
start_epoch = int(args.load_pthpath.split("_")[-1][:-4])
model_state_dict, optimizer_state_dict = load_checkpoint(args.load_pthpath)
if isinstance(model, nn.DataParallel):
model.module.load_state_dict(model_state_dict)
else:
model.load_state_dict(model_state_dict)
optimizer.load_state_dict(optimizer_state_dict)
print("Loaded model from {}".format(args.load_pthpath))
# ================================================================================================
# TRAINING LOOP
# ================================================================================================
# Forever increasing counter keeping track of iterations completed (for tensorboard logging).
global_iteration_step = start_epoch * iterations
for epoch in range(start_epoch, config["solver"]["num_epochs"]):
# --------------------------------------------------------------------------------------------
# ON EPOCH START (combine dataloaders if training on train + val)
# --------------------------------------------------------------------------------------------
if config["solver"]["training_splits"] == "trainval":
combined_dataloader = itertools.chain(train_dataloader, val_dataloader)
else:
combined_dataloader = itertools.chain(train_dataloader)
print(f"\nTraining for epoch {epoch}:")
for i, batch in enumerate(tqdm(combined_dataloader)):
for key in batch:
batch[key] = batch[key].to(device)
optimizer.zero_grad()
output = model(batch)
batch_loss = criterion(output.view(-1, output.size(-1)), batch["ans_ind"].view(-1))
batch_loss.backward()
optimizer.step()
summary_writer.add_scalar("train/loss", batch_loss, global_iteration_step)
summary_writer.add_scalar("train/lr", optimizer.param_groups[0]["lr"], global_iteration_step)
if global_iteration_step <= iterations * config["solver"]["warmup_epochs"]:
scheduler.step(global_iteration_step)
else:
global_iteration_step_in_2 = iterations * config["solver"]["warmup_epochs"] + 1 - global_iteration_step
scheduler2.step(int(global_iteration_step_in_2))
global_iteration_step += 1
torch.cuda.empty_cache()
# --------------------------------------------------------------------------------------------
# ON EPOCH END (checkpointing and validation)
# --------------------------------------------------------------------------------------------
checkpoint_manager.step()
# Validate and report automatic metrics.
if args.validate:
# Switch dropout, batchnorm etc to the correct mode.
model.eval()
print(f"\nValidation after epoch {epoch}:")
for i, batch in enumerate(tqdm(val_dataloader)):
for key in batch:
batch[key] = batch[key].to(device)
with torch.no_grad():
output = model(batch)
sparse_metrics.observe(output, batch["ans_ind"])
if "gt_relevance" in batch:
output = output[torch.arange(output.size(0)), batch["round_id"] - 1, :]
ndcg.observe(output, batch["gt_relevance"])
all_metrics = {}
all_metrics.update(sparse_metrics.retrieve(reset=True))
all_metrics.update(ndcg.retrieve(reset=True))
for metric_name, metric_value in all_metrics.items():
print(f"{metric_name}: {metric_value}")
summary_writer.add_scalars("metrics", all_metrics, global_iteration_step)
model.train()
torch.cuda.empty_cache()