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import math
# Simplified config for inference
config_text = """
DATA:
NUM_FRAMES: 8
SAMPLING_RATE: 8
TEST_CROP_SIZE: 256
INPUT_CHANNEL_NUM: [3]
RESNET:
ZERO_INIT_FINAL_BN: True
WIDTH_PER_GROUP: 64
NUM_GROUPS: 1
DEPTH: 50
TRANS_FUNC: bottleneck_transform
STRIDE_1X1: False
NUM_BLOCK_TEMP_KERNEL: [[3], [4], [6], [3]]
NONLOCAL:
LOCATION: [[[]], [[]], [[]], [[]]]
GROUP: [[1], [1], [1], [1]]
INSTANTIATION: softmax
BN:
USE_PRECISE_STATS: True
NUM_BATCHES_PRECISE: 200
MODEL:
NUM_CLASSES: 1
ARCH: i3d
MODEL_NAME: ResNet
DROPOUT_RATE: 0.1
HEAD_ACT: sigmoid
TEST:
ENABLE: True
DATASET: kinetics
BATCH_SIZE: 64
DATA_LOADER:
NUM_WORKERS: 8
PIN_MEMORY: True
NUM_GPUS: 8
NUM_SHARDS: 1
RNG_SEED: 0
OUTPUT_DIR: .
"""
# from .f3net import FAD_Head
from slowfast.models.video_model_builder import ResNet as ResNetOri
from slowfast.config.defaults import get_cfg
import torch
from torch import nn
from config_ftcn import config as my_cfg
from inspect import signature
# Removed TimeTransformer import - not used directly in this file
# Removed random import - not used in inference
my_cfg.init_with_yaml()
my_cfg.update_with_yaml("ftcn_tt.yaml")
my_cfg.freeze()
class CenterPatchPool(nn.Module):
"""Simplified patch pooling for inference - always use center patch"""
def __init__(self):
super().__init__()
def forward(self, x):
# batch,channel,16,7x7
b, c, t, h, w = x.shape
x = x.reshape(b, c, t, h * w)
idx = h * w // 2 # Always use center patch for inference
x = x[..., idx]
return x
def valid_idx(idx, h):
i = idx // h
j = idx % h
if j == 0 or i == h - 1 or j == h - 1:
return False
else:
return True
class CenterAvgPool(nn.Module):
"""Simplified average pooling for inference - use all valid patches"""
def __init__(self):
super().__init__()
def forward(self, x):
# batch,channel,16,7x7
b, c, t, h, w = x.shape
x = x.reshape(b, c, t, h * w)
candidates = list(range(h * w))
candidates = [idx for idx in candidates if valid_idx(idx, h)]
x = x[..., candidates].mean(-1)
return x
# Removed duplicate TransformerHead class - using the one from temporal_transformer.py
parameters = [parameter for parameter in signature(nn.Conv3d).parameters]
spatial_count = my_cfg.model.inco.spatial_count
keep_stride_count = my_cfg.model.inco.keep_stride_count
def temporal_only_conv(module, name, removed, stride_removed=0):
"""
Recursively put desired batch norm in nn.module module.
set module = net to start code.
"""
# go through all attributes of module nn.module (e.g. network or layer) and put batch norms if present
for attr_str in dir(module):
sub_module = getattr(module, attr_str)
if type(sub_module) == nn.Conv3d:
target_spatial_size = 1
predefine_padding = {1: 0, 3: 1, 5: 2, 7: 3}
kernel_size = list(sub_module.kernel_size)
assert kernel_size[1] == kernel_size[2]
stride = sub_module.stride
extra = None
if stride[1] == stride[2] == 2:
stride_removed += 1
if stride_removed > keep_stride_count:
stride = [1, 1, 1]
extra = nn.MaxPool3d((1, 2, 2))
if kernel_size[1] == 1 and extra is None:
continue
padding = list(sub_module.padding)
kernel_size[1] = kernel_size[2] = target_spatial_size
padding[1] = padding[2] = predefine_padding[target_spatial_size]
if 'device' in parameters:
parameters.remove('device')
if 'dtype' in parameters:
parameters.remove('dtype')
param_dict = {key: getattr(sub_module, key) for key in parameters}
param_dict.update(kernel_size=kernel_size, padding=padding, stride=stride)
conv = nn.Conv3d(**param_dict)
new_module = conv
removed += 1
if removed > spatial_count:
setattr(module, attr_str, new_module)
if extra is not None:
if attr_str == "conv":
bn_str = "bn"
else:
bn_str = f"{attr_str}_bn"
bn_module = getattr(module, bn_str)
assert isinstance(bn_module, nn.BatchNorm3d)
new_bn_module = nn.Sequential(bn_module, extra)
setattr(module, bn_str, new_bn_module)
else:
print("keep spatial")
elif type(sub_module) == nn.Dropout:
new_module = nn.Dropout(p=0.5)
setattr(module, attr_str, new_module)
if my_cfg.model.inco.no_time_pool:
if type(sub_module) == nn.MaxPool3d:
kernel_size = list(sub_module.kernel_size)
if kernel_size[0] == 2:
kernel_size[0] = 1
setattr(module, attr_str, nn.MaxPool3d(kernel_size))
elif type(sub_module) == nn.AvgPool3d:
kernel_size = list(sub_module.kernel_size)
kernel_size[0] = 2 * kernel_size[0]
setattr(module, attr_str, nn.AvgPool3d(kernel_size))
# iterate through immediate child modules. Note, the recursion is done by our code no need to use named_modules()
old_name = name
for name, immediate_child_module in module.named_children():
removed, stride_removed = temporal_only_conv(
immediate_child_module, old_name + "." + name, removed, stride_removed
)
return removed, stride_removed
class I3D8x8(nn.Module):
def __init__(self) -> None:
super(I3D8x8, self).__init__()
cfg = get_cfg()
cfg.merge_from_str(config_text)
cfg.NUM_GPUS = 1
cfg.TEST.BATCH_SIZE = 1
cfg.TRAIN.BATCH_SIZE = 1
cfg.DATA.NUM_FRAMES = my_cfg.clip_size
SOLVER = my_cfg.model.inco.SOLVER
if SOLVER is not None:
for key, val in SOLVER.to_dict().items():
old_val = getattr(cfg.SOLVER, key)
val = type(old_val)(val)
setattr(cfg.SOLVER, key, val)
if my_cfg.model.inco.i3d_routine:
self.cfg = cfg
self.resnet = ResNetOri(cfg)
temporal_only_conv(self.resnet, "model", 0)
stop_point = my_cfg.model.transformer.stop_point
for i in [5, 4, 3]:
if stop_point <= i:
setattr(self.resnet, f"s{i}", nn.Identity())
if stop_point == 3:
setattr(self.resnet, f"pathway0_pool", nn.Identity())
gc.collect()
torch.cuda.empty_cache()
def forward(
self,
images, ft_features=None,
freeze_backbone=False
):
assert not freeze_backbone
inputs = [images]
pred = self.resnet(inputs, ft_features)
return pred
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