Skip to content

Commit

Permalink
Formatting
Browse files Browse the repository at this point in the history
  • Loading branch information
@yan-gao-GY
yan-gao-GY committed Nov 30, 2023
1 parent 212e626 commit f895c01
Show file tree
Hide file tree
Showing 4 changed files with 158 additions and 158 deletions.
60 changes: 30 additions & 30 deletions baselines/fedvssl/fedvssl/conf/mmcv_conf/finetuning/model_r3d18.py
View file
Original file line number Diff line number Diff line change
@@ -1,32 +1,32 @@
"""Config file used for fine-tuning on UCF-101 dataset."""

model = dict(
type="TSN",
backbone=dict(
type="R3D",
depth=18,
num_stages=4,
stem=dict(
temporal_kernel_size=3,
temporal_stride=1,
in_channels=3,
with_pool=False,
),
down_sampling=[False, True, True, True],
channel_multiplier=1.0,
bottleneck_multiplier=1.0,
with_bn=True,
zero_init_residual=False,
pretrained=None,
),
st_module=dict(spatial_type="avg", temporal_size=2, spatial_size=7),
cls_head=dict(
with_avg_pool=False,
temporal_feature_size=1,
spatial_feature_size=1,
dropout_ratio=0.5,
in_channels=512,
init_std=0.001,
num_classes=101,
),
)
model = {
"type": "TSN",
"backbone": {
"type": "R3D",
"depth": 18,
"num_stages": 4,
"stem": {
"temporal_kernel_size": 3,
"temporal_stride": 1,
"in_channels": 3,
"with_pool": False,
},
"down_sampling": [False, True, True, True],
"channel_multiplier": 1.0,
"bottleneck_multiplier": 1.0,
"with_bn": True,
"zero_init_residual": False,
"pretrained": None,
},
"st_module": {"spatial_type": "avg", "temporal_size": 2, "spatial_size": 7},
"cls_head": {
"with_avg_pool": False,
"temporal_feature_size": 1,
"spatial_feature_size": 1,
"dropout_ratio": 0.5,
"in_channels": 512,
"init_std": 0.001,
"num_classes": 101,
},
}
10 changes: 5 additions & 5 deletions baselines/fedvssl/fedvssl/conf/mmcv_conf/finetuning/r3d_18_ucf101/finetune_ucf101.py
View file
Original file line number Diff line number Diff line change
Expand Up @@ -4,8 +4,8 @@

work_dir = "./finetune_results/"

model = dict(
backbone=dict(
pretrained="./model_pretrained.pth",
),
)
model = {
"backbone": {
"pretrained": "./model_pretrained.pth",
},
}
10 changes: 5 additions & 5 deletions baselines/fedvssl/fedvssl/conf/mmcv_conf/finetuning/r3d_18_ucf101/test_ucf101.py
View file
Original file line number Diff line number Diff line change
Expand Up @@ -4,8 +4,8 @@

work_dir = "./finetune_results/"

model = dict(
backbone=dict(
pretrained="finetune_results/epoch_150.pth",
),
)
model = {
"backbone": {
"pretrained": "finetune_results/epoch_150.pth",
},
}
236 changes: 118 additions & 118 deletions baselines/fedvssl/fedvssl/conf/mmcv_conf/finetuning/runtime_ucf101.py
View file
Original file line number Diff line number Diff line change
@@ -1,137 +1,137 @@
"""Config file used for fine-tuning on UCF-101 dataset."""

dist_params = dict(backend="nccl")
dist_params = {"backend": "nccl"}
log_level = "INFO"
load_from = None
resume_from = None
syncbn = True

train_cfg = None
test_cfg = None
evaluation = dict(interval=10)
evaluation = {"interval": 10}

data = dict(
videos_per_gpu=4, # total batch size 8*4 == 32
workers_per_gpu=4,
train=dict(
type="TSNDataset",
name="ucf101_train_split1",
data_source=dict(
type="JsonClsDataSource",
ann_file="ucf101/annotations/train_split_1.json",
),
backend=dict(
type="ZipBackend",
zip_fmt="ucf101/zips/{}.zip",
frame_fmt="img_{:05d}.jpg",
),
frame_sampler=dict(
type="RandomFrameSampler",
num_clips=1,
clip_len=16,
strides=2,
temporal_jitter=False,
),
test_mode=False,
transform_cfg=[
dict(type="GroupScale", scales=[(149, 112), (171, 128), (192, 144)]),
dict(type="GroupFlip", flip_prob=0.35),
dict(type="RandomBrightness", prob=0.20, delta=32),
dict(type="RandomContrast", prob=0.20, delta=0.20),
dict(
type="RandomHueSaturation",
prob=0.20,
hue_delta=12,
saturation_delta=0.1,
),
dict(type="GroupRandomCrop", out_size=112),
dict(
type="GroupToTensor",
switch_rgb_channels=True,
div255=True,
mean=(0.485, 0.456, 0.406),
std=(0.229, 0.224, 0.225),
),
data = {
"videos_per_gpu": 4, # total batch size 8*4 == 32
"workers_per_gpu": 4,
"train": {
"type": "TSNDataset",
"name": "ucf101_train_split1",
"data_source": {
"type": "JsonClsDataSource",
"ann_file": "ucf101/annotations/train_split_1.json",
},
"backend": {
"type": "ZipBackend",
"zip_fmt": "ucf101/zips/{}.zip",
"frame_fmt": "img_{:05d}.jpg",
},
"frame_sampler": {
"type": "RandomFrameSampler",
"num_clips": 1,
"clip_len": 16,
"strides": 2,
"temporal_jitter": False,
},
"test_mode": False,
"transform_cfg": [
{"type": "GroupScale", "scales": [(149, 112), (171, 128), (192, 144)]},
{"type": "GroupFlip", "flip_prob": 0.35},
{"type": "RandomBrightness", "prob": 0.20, "delta": 32},
{"type": "RandomContrast", "prob": 0.20, "delta": 0.20},
{
"type": "RandomHueSaturation",
"prob": 0.20,
"hue_delta": 12,
"saturation_delta": 0.1,
},
{"type": "GroupRandomCrop", "out_size": 112},
{
"type": "GroupToTensor",
"switch_rgb_channels": True,
"div255": True,
"mean": (0.485, 0.456, 0.406),
"std": (0.229, 0.224, 0.225),
},
],
),
val=dict(
type="TSNDataset",
name="ucf101_test_split1",
data_source=dict(
type="JsonClsDataSource",
ann_file="ucf101/annotations/test_split_1.json",
),
backend=dict(
type="ZipBackend",
zip_fmt="ucf101/zips/{}.zip",
frame_fmt="img_{:05d}.jpg",
),
frame_sampler=dict(
type="UniformFrameSampler",
num_clips=10,
clip_len=16,
strides=2,
temporal_jitter=False,
),
test_mode=True,
transform_cfg=[
dict(type="GroupScale", scales=[(171, 128)]),
dict(type="GroupCenterCrop", out_size=112),
dict(
type="GroupToTensor",
switch_rgb_channels=True,
div255=True,
mean=(0.485, 0.456, 0.406),
std=(0.229, 0.224, 0.225),
),
},
"val": {
"type": "TSNDataset",
"name": "ucf101_test_split1",
"data_source": {
"type": "JsonClsDataSource",
"ann_file": "ucf101/annotations/test_split_1.json",
},
"backend": {
"type": "ZipBackend",
"zip_fmt": "ucf101/zips/{}.zip",
"frame_fmt": "img_{:05d}.jpg",
},
"frame_sampler": {
"type": "UniformFrameSampler",
"num_clips": 10,
"clip_len": 16,
"strides": 2,
"temporal_jitter": False,
},
"test_mode": True,
"transform_cfg": [
{"type": "GroupScale", "scales": [(171, 128)]},
{"type": "GroupCenterCrop", "out_size": 112},
{
"type": "GroupToTensor",
"switch_rgb_channels": True,
"div255": True,
"mean": (0.485, 0.456, 0.406),
"std": (0.229, 0.224, 0.225),
},
],
),
test=dict(
type="TSNDataset",
name="ucf101_test_split1",
data_source=dict(
type="JsonClsDataSource",
ann_file="ucf101/annotations/test_split_1.json",
),
backend=dict(
type="ZipBackend",
zip_fmt="ucf101/zips/{}.zip",
frame_fmt="img_{:05d}.jpg",
),
frame_sampler=dict(
type="UniformFrameSampler",
num_clips=10,
clip_len=16,
strides=2,
temporal_jitter=False,
),
test_mode=True,
transform_cfg=[
dict(type="GroupScale", scales=[(171, 128)]),
dict(type="GroupCenterCrop", out_size=112),
dict(
type="GroupToTensor",
switch_rgb_channels=True,
div255=True,
mean=(0.485, 0.456, 0.406),
std=(0.229, 0.224, 0.225),
),
},
"test": {
"type": "TSNDataset",
"name": "ucf101_test_split1",
"data_source": {
"type": "JsonClsDataSource",
"ann_file": "ucf101/annotations/test_split_1.json",
},
"backend": {
"type": "ZipBackend",
"zip_fmt": "ucf101/zips/{}.zip",
"frame_fmt": "img_{:05d}.jpg",
},
"frame_sampler": {
"type": "UniformFrameSampler",
"num_clips": 10,
"clip_len": 16,
"strides": 2,
"temporal_jitter": False,
},
"test_mode": True,
"transform_cfg": [
{"type": "GroupScale", "scales": [(171, 128)]},
{"type": "GroupCenterCrop", "out_size": 112},
{
"type": "GroupToTensor",
"switch_rgb_channels": True,
"div255": True,
"mean": (0.485, 0.456, 0.406),
"std": (0.229, 0.224, 0.225),
},
],
),
)
},
}

# optimizer
total_epochs = 150
optimizer = dict(type="SGD", lr=0.01, momentum=0.9, weight_decay=5e-4)
optimizer_config = dict(grad_clip=dict(max_norm=40, norm_type=2))
optimizer = {"type": "SGD", "lr": 0.01, "momentum": 0.9, "weight_decay": 5e-4}
optimizer_config = {"grad_clip": {"max_norm": 40, "norm_type": 2}}
# learning policy
lr_config = dict(policy="step", step=[60, 120])
checkpoint_config = dict(interval=1, max_keep_ckpts=1, create_symlink=False)
lr_config = {"policy": "step", "step": [60, 120]}
checkpoint_config = {"interval": 1, "max_keep_ckpts": 1, "create_symlink": False}
workflow = [("train", 50)]
log_config = dict(
interval=10,
hooks=[
dict(type="TextLoggerHook"),
dict(type="TensorboardLoggerHook"),
log_config = {
"interval": 10,
"hooks": [
{"type": "TextLoggerHook"},
{"type": "TensorboardLoggerHook"},
],
)
}

0 comments on commit f895c01

Please sign in to comment.