-
Notifications
You must be signed in to change notification settings - Fork 3
/
train.py
300 lines (252 loc) · 14.4 KB
/
train.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
import os
import cv2
import time
import tqdm
import torch
import random
import socket
import argparse
import torch.nn.functional as F
import torch.distributed as dist
import torch.multiprocessing as mp
import lookforthechange
from method.dataset import ChangeItVideoDataset, identity_collate, DistributedDropFreeSampler
from method.model import ClipClassifier
from method.utils import get_cosine_schedule_with_warmup, AverageMeter, select_correct_classes, JointMeter
cv2.setNumThreads(1) # do not spawn multiple threads for augmentation (ffmpeg then raises an error)
def main(args):
ngpus_per_node = torch.cuda.device_count()
node_count = int(os.environ.get("SLURM_NPROCS", "1"))
node_rank = int(os.environ.get("SLURM_PROCID", "0"))
job_id = os.environ.get("SLURM_JOBID", "0")
if node_count == 1: # for PBS/PMI clusters
node_count = int(os.environ.get("PMI_SIZE", "1"))
node_rank = int(os.environ.get("PMI_RANK", "0"))
job_id = os.environ.get("PBS_JOBID", "".join([str(random.randint(0, 9)) for _ in range(5)]))
dist_url = "file://{}.{}".format(os.path.realpath("distfile"), job_id)
print(f"Hi from node {socket.gethostname()} ({node_rank}/{node_count} with {ngpus_per_node} GPUs)!", flush=True)
mp.spawn(main_worker, nprocs=ngpus_per_node, args=({
"ngpus_per_node": ngpus_per_node,
"node_count": node_count,
"node_rank": node_rank,
"dist_url": dist_url,
"job_id": job_id
}, args))
def main_worker(local_rank, cluster_args, args):
world_size = cluster_args["node_count"] * cluster_args["ngpus_per_node"]
global_rank = cluster_args["node_rank"] * cluster_args["ngpus_per_node"] + local_rank
dist.init_process_group(
backend="nccl",
init_method=cluster_args["dist_url"],
world_size=world_size,
rank=global_rank,
)
if global_rank == 0:
for k, v in sorted(vars(args).items(), key=lambda x: x[0]):
print(f"# {k}: {v}")
print(f"# effective_batch_size: {world_size * args.local_batch_size}", flush=True)
###############
# DATASET
###############
train_ds = ChangeItVideoDataset(
video_roots=args.video_roots, annotation_root=os.path.join(args.dataset_root, "annotations"),
file_mode="unannotated", noise_adapt_weight_root=None if args.ignore_video_weight else os.path.join(args.dataset_root, "videos"),
noise_adapt_weight_threshold_file=None if args.ignore_video_weight else os.path.join(args.dataset_root, "categories.csv"), augment=args.augment
)
test_ds = ChangeItVideoDataset(
video_roots=args.video_roots, annotation_root=os.path.join(args.dataset_root, "annotations"),
file_mode="annotated", noise_adapt_weight_root=None if args.ignore_video_weight else os.path.join(args.dataset_root, "videos"),
noise_adapt_weight_threshold_file=None if args.ignore_video_weight else os.path.join(args.dataset_root, "categories.csv"), augment=False
)
if global_rank == 0:
print(train_ds, test_ds, sep="\n", flush=True)
train_sampler = torch.utils.data.distributed.DistributedSampler(
train_ds, shuffle=True, drop_last=True) if world_size > 1 else None
train_ds_iter = torch.utils.data.DataLoader(
train_ds, batch_size=args.local_batch_size, shuffle=world_size == 1, drop_last=True, num_workers=2,
pin_memory=True, sampler=train_sampler, collate_fn=identity_collate)
test_sampler = DistributedDropFreeSampler(test_ds, shuffle=False) if world_size > 1 else None
test_ds_iter = torch.utils.data.DataLoader(
test_ds, batch_size=1, shuffle=False, drop_last=False, num_workers=2,
pin_memory=True, sampler=test_sampler, collate_fn=identity_collate)
###############
# MODEL
###############
weights = torch.jit.load(args.clip_weights, map_location="cpu").state_dict()
model = ClipClassifier(hidden_mlp_layers=[4096],
params=weights,
n_classes=train_ds.n_classes,
train_backbone=args.train_backbone)
assert model.backbone.input_resolution == 224
torch.cuda.set_device(local_rank)
model.cuda(local_rank)
model_parallel = torch.nn.parallel.DistributedDataParallel(model, device_ids=[local_rank])
###############
# OPTIMIZER
###############
head_params = model_parallel.module.heads.parameters()
optim_head = torch.optim.SGD(head_params, lr=args.lr, momentum=0.9, weight_decay=args.weight_decay)
scheduler_head = get_cosine_schedule_with_warmup(optim_head, 5 * len(train_ds_iter), len(train_ds_iter) * args.n_epochs)
if args.train_backbone:
backbone_params = model_parallel.module.backbone.parameters()
optim_backbone = torch.optim.AdamW(backbone_params, lr=args.lr_backbone, weight_decay=args.weight_decay_backbone)
scheduler_backbone = get_cosine_schedule_with_warmup(optim_backbone, 5 * len(train_ds_iter), len(train_ds_iter) * args.n_epochs)
###############
# TRAINING
###############
n_frames_per_gt = args.n_frames_per_gt
kappa_dist = 60
loss_metric = AverageMeter()
loss_norm_metric = AverageMeter()
unsup_state_loss_metric = AverageMeter()
unsup_action_loss_metric = AverageMeter()
for epoch in range(1, args.n_epochs + 1):
if world_size > 1: train_sampler.set_epoch(epoch)
loss_metric.reset()
loss_norm_metric.reset()
unsup_state_loss_metric.reset()
unsup_action_loss_metric.reset()
iterator = tqdm.tqdm(train_ds_iter) if global_rank == 0 else train_ds_iter
for batch in iterator: # id, class, video, annotation/None, weight
optim_head.zero_grad()
if args.train_backbone: optim_backbone.zero_grad()
# COMPUTE GT FOR ALL VIDEOS IN BATCH
batch_for_training = []
for _, class_, inputs, _, weight in batch:
classes = torch.LongTensor([class_])
# PREDICT
with torch.no_grad():
predictions = []
for i in range(0, len(inputs), 256):
predictions += [model(inputs[i:i + 256].cuda(local_rank))]
predictions = {
"state": torch.cat([p["state"] for p in predictions], dim=0),
"action": torch.cat([p["action"] for p in predictions], dim=0)
}
st_probs = select_correct_classes(
torch.softmax(predictions["state"].unsqueeze(0), -1), classes, n_classes=train_ds.n_classes)
ac_probs = select_correct_classes(
torch.softmax(predictions["action"].unsqueeze(0), -1), classes, n_classes=train_ds.n_classes + 1)
# COMPUTE GROUND TRUTH
indices = lookforthechange.optimal_state_change_indices(
st_probs, ac_probs, lens=torch.tensor([st_probs.shape[1]], dtype=torch.int32, device=st_probs.device))
indices = indices.view(1, 3).cpu() # [S1idx, S2idx, ACidx]
positives = indices.repeat(n_frames_per_gt, 1) + \
torch.arange(-(n_frames_per_gt // 2), (n_frames_per_gt // 2) + 1, 1, device=indices.device).unsqueeze_(1)
indices_extended = torch.cat([
positives.transpose(1, 0).reshape(-1), positives[:, 2] - kappa_dist, positives[:, 2] + kappa_dist
], 0).clamp_(0, len(inputs) - 1)
# [ S1idx - 1, S1idx, S1idx + 1,
# S2idx - 1, S2idx, S2idx + 1,
# ACidx - 1, ACidx, ACidx + 1,
# ACidx - 61, ACidx - 60, ACidx - 59,
# ACidx + 59, ACidx + 60, ACidx + 61]
bg_class_index = train_ds.n_classes
action_targets = torch.LongTensor([bg_class_index] * n_frames_per_gt * 2 +
[class_] * n_frames_per_gt +
[bg_class_index] * n_frames_per_gt * 2)
# [ BG, BG, BG,
# BG, BG, BG,
# CLS, CLS, CLS,
# BG, BG, BG,
# BG, BG, BG]
bg_class_index = train_ds.n_classes * 2
state_targets = torch.LongTensor([class_ * 2 + 0] * n_frames_per_gt +
[class_ * 2 + 1] * n_frames_per_gt +
[bg_class_index] * n_frames_per_gt +
[bg_class_index] * n_frames_per_gt * 2)
state_target_mask = torch.FloatTensor([1.] * n_frames_per_gt * 3 + [0.] * n_frames_per_gt * 2)
# [ CS1, CS1, CS1,
# CS2, CS2, CS2,
# BG, BG, BG,
# *, *, *,
# *, *, *]
batch_for_training.append((
inputs[indices_extended], action_targets, state_targets, state_target_mask, weight))
# FORWARD + BACKWARD PASS
predictions = model_parallel(torch.cat([x[0] for x in batch_for_training], 0).cuda(local_rank))
if batch_for_training[0][4] is None:
video_loss_weight = torch.FloatTensor([1. for _ in batch_for_training]).view(-1, 1).cuda(local_rank)
else:
video_loss_weight = torch.FloatTensor([x[4] for x in batch_for_training]) * (-1 / 0.001)
video_loss_weight = 1 / (1 + torch.exp(video_loss_weight))
video_loss_weight = video_loss_weight.view(-1, 1).cuda(local_rank)
state_gt = torch.cat([x[2] for x in batch_for_training], 0).cuda(local_rank)
state_gt_mask = torch.cat([x[3] for x in batch_for_training], 0).cuda(local_rank)
action_gt = torch.cat([x[1] for x in batch_for_training], 0).cuda(local_rank)
state_loss = F.cross_entropy(predictions["state"], state_gt, reduction="none") * state_gt_mask
state_loss = state_loss.view(-1, n_frames_per_gt * 5) * video_loss_weight
action_loss = F.cross_entropy(predictions["action"], action_gt, reduction="none")
action_loss = action_loss.view(-1, n_frames_per_gt * 5) * video_loss_weight
state_loss = torch.sum(state_loss)
action_loss = 0.2 * torch.sum(action_loss)
loss = state_loss + action_loss
# DistributedDataParallel does gradient averaging, i.e. loss is x-times smaller than in Look for the Change.
# When training with frozen backbone, make it somewhat equivalent to the Look for the Change setup.
if not args.train_backbone:
loss = loss * world_size
loss.backward()
optim_head.step()
scheduler_head.step()
if args.train_backbone:
optim_backbone.step()
scheduler_backbone.step()
loss_metric.update(loss.item(), len(batch_for_training))
unsup_state_loss_metric.update(state_loss.item(), len(batch_for_training))
unsup_action_loss_metric.update(action_loss.item(), len(batch_for_training))
###############
# VALIDATION
###############
joint_meter = JointMeter(train_ds.n_classes)
for batch in test_ds_iter:
_, class_, inputs, annot, _ = batch[0]
classes = torch.LongTensor([class_])
with torch.no_grad():
predictions = model(inputs.cuda(local_rank))
st_probs = select_correct_classes(
torch.softmax(predictions["state"].unsqueeze(0), -1), classes, n_classes=train_ds.n_classes)
ac_probs = select_correct_classes(
torch.softmax(predictions["action"].unsqueeze(0), -1), classes, n_classes=train_ds.n_classes + 1)
joint_meter.log(ac_probs[0, :, 0].cpu().numpy(), st_probs[0].cpu().numpy(), annot, category=class_)
vallog_fn = "{}.{}".format(os.path.realpath("vallog"), cluster_args["job_id"])
joint_meter.dump(vallog_fn, global_rank)
dist.barrier()
if global_rank == 0:
time.sleep(10)
joint_meter.load(vallog_fn)
dir_ = f'logs/{cluster_args["job_id"]}'
os.makedirs(dir_, exist_ok=True)
torch.save({"state_dict": model_parallel.state_dict(), "args": model.args, "n_classes": model.n_classes,
"hidden_mlp_layers": model.hidden_mlp_layers}, f"{dir_}/epoch{epoch:03d}.pth")
print(f"Epoch {epoch} ("
f"T loss: {loss_metric.value:.3f}, "
f"T lr: {scheduler_head.get_last_lr()[0]:.6f}, "
f"T grad norm: {loss_norm_metric.value:.1f}, "
f"T unsup state loss: {unsup_state_loss_metric.value:.3f}, "
f"T unsup action loss: {unsup_action_loss_metric.value:.3f}, "
f"V state acc: {joint_meter.acc:.1f}%, "
f"V state prec: {joint_meter.sp:.1f}%, "
f"V state joint prec: {joint_meter.jsp:.1f}%, "
f"V action prec: {joint_meter.ap:.1f}%, "
f"V action joint prec: {joint_meter.jap:.1f}%)", flush=True)
print("> {:20} {:>6} {:>6} {:>6} {:>6} {:>6}".format("CATEGORY", "SAcc", "SP", "JtSP", "AP", "JtAP"))
print("\n".join([
"> {:20}{:6.1f}%{:6.1f}%{:6.1f}%{:6.1f}%{:6.1f}%".format(cls_name, *joint_meter[train_ds.classes[cls_name]])
for cls_name in sorted(train_ds.classes.keys())
]), flush=True)
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument("--video_roots", type=str, nargs="+", default=["./videos"])
parser.add_argument("--dataset_root", type=str, default="./ChangeIt")
parser.add_argument("--lr", default=0.0001, type=float)
parser.add_argument("--lr_backbone", default=0.00001, type=float)
parser.add_argument("--weight_decay", type=float, default=0.001)
parser.add_argument("--weight_decay_backbone", type=float, default=0.)
parser.add_argument("--train_backbone", action="store_true")
parser.add_argument("--n_frames_per_gt", type=int, default=3)
parser.add_argument("--local_batch_size", type=int, default=2)
parser.add_argument("--clip_weights", type=str, default="./weights/ViT-L-14.pt")
parser.add_argument("--n_epochs", type=int, default=20)
parser.add_argument("--augment", action="store_true")
parser.add_argument("--ignore_video_weight", action="store_true")
main(parser.parse_args())