def _construct_network(self, cfg):
"""
Builds a SlowFast model.
Args:
cfg (CfgNode): model building configs, details are in the
comments of the config file.
"""
_MODEL_STAGE_DEPTH = {50: (3, 4, 6, 3), 101: (3, 4, 23, 3)}
# Params from configs.
norm_module = get_norm(cfg)
pool_size = _POOL1[cfg.MODEL.ARCH]
num_groups = cfg.RESNET.NUM_GROUPS
width_per_group = cfg.RESNET.WIDTH_PER_GROUP
spatial_dilations = cfg.RESNET.SPATIAL_DILATIONS
spatial_strides = cfg.RESNET.SPATIAL_STRIDES
temp_kernel = _TEMPORAL_KERNEL_BASIS[cfg.MODEL.ARCH]
num_block_temp_kernel = cfg.RESNET.NUM_BLOCK_TEMP_KERNEL
stage_depth = _MODEL_STAGE_DEPTH[cfg.RESNET.DEPTH]
stage_conv_a_kernel_sizes = [[], []]
for pathway in range(2):
for stage in range(4):
stage_conv_a_kernel_sizes[pathway].append(
((temp_kernel[stage + 1][pathway][0], 1, 1),)
* num_block_temp_kernel[stage][pathway]
+ ((1, 1, 1),)
* (
stage_depth[stage]
- num_block_temp_kernel[stage][pathway]
)
)
# Head from config
# Number of stages = 4
stage_dim_in = cfg.RESNET.WIDTH_PER_GROUP * 2 ** (4 + 1)
head_in_features = stage_dim_in + stage_dim_in // cfg.SLOWFAST.BETA_INV
if cfg.DETECTION.ENABLE:
self.detection_head = create_res_roi_pooling_head(
in_features=head_in_features,
out_features=cfg.MODEL.NUM_CLASSES,
pool=None,
output_size=(1, 1, 1),
dropout_rate=cfg.MODEL.DROPOUT_RATE,
activation=None,
output_with_global_average=False,
pool_spatial=nn.MaxPool2d,
resolution=[cfg.DETECTION.ROI_XFORM_RESOLUTION] * 2,
spatial_scale=1.0 / float(cfg.DETECTION.SPATIAL_SCALE_FACTOR),
sampling_ratio=0,
roi=ROIAlign,
)
head_pool_kernel_sizes = (
(
cfg.DATA.NUM_FRAMES
// cfg.SLOWFAST.ALPHA
// pool_size[0][0],
1,
1,
),
(cfg.DATA.NUM_FRAMES // pool_size[1][0], 1, 1),
)
else:
head_pool_kernel_sizes = (
(
cfg.DATA.NUM_FRAMES
// cfg.SLOWFAST.ALPHA
// pool_size[0][0],
cfg.DATA.TRAIN_CROP_SIZE // 32 // pool_size[0][1],
cfg.DATA.TRAIN_CROP_SIZE // 32 // pool_size[0][2],
),
(
cfg.DATA.NUM_FRAMES // pool_size[1][0],
cfg.DATA.TRAIN_CROP_SIZE // 32 // pool_size[1][1],
cfg.DATA.TRAIN_CROP_SIZE // 32 // pool_size[1][2],
),
)
self.model = create_slowfast(
# SlowFast configs.
slowfast_channel_reduction_ratio=cfg.SLOWFAST.BETA_INV,
slowfast_conv_channel_fusion_ratio=cfg.SLOWFAST.FUSION_CONV_CHANNEL_RATIO,
slowfast_fusion_conv_kernel_size=(
cfg.SLOWFAST.FUSION_KERNEL_SZ,
1,
1,
),
slowfast_fusion_conv_stride=(cfg.SLOWFAST.ALPHA, 1, 1),
# Input clip configs.
input_channels=cfg.DATA.INPUT_CHANNEL_NUM,
# Model configs.
model_depth=cfg.RESNET.DEPTH,
model_num_class=cfg.MODEL.NUM_CLASSES,
dropout_rate=cfg.MODEL.DROPOUT_RATE,
# Normalization configs.
norm=norm_module,
# Activation configs.
activation=partial(nn.ReLU, inplace=cfg.RESNET.INPLACE_RELU),
# Stem configs.
stem_dim_outs=(
width_per_group,
width_per_group // cfg.SLOWFAST.BETA_INV,
),
stem_conv_kernel_sizes=(
(temp_kernel[0][0][0], 7, 7),
(temp_kernel[0][1][0], 7, 7),
),
stem_conv_strides=((1, 2, 2), (1, 2, 2)),
stem_pool=nn.MaxPool3d,
stem_pool_kernel_sizes=((1, 3, 3), (1, 3, 3)),
stem_pool_strides=((1, 2, 2), (1, 2, 2)),
# Stage configs.
stage_conv_a_kernel_sizes=stage_conv_a_kernel_sizes,
stage_conv_b_kernel_sizes=(
((1, 3, 3), (1, 3, 3), (1, 3, 3), (1, 3, 3)),
((1, 3, 3), (1, 3, 3), (1, 3, 3), (1, 3, 3)),
),
stage_conv_b_num_groups=(
(num_groups, num_groups, num_groups, num_groups),
(num_groups, num_groups, num_groups, num_groups),
),
stage_conv_b_dilations=(
(
(1, spatial_dilations[0][0], spatial_dilations[0][0]),
(1, spatial_dilations[1][0], spatial_dilations[1][0]),
(1, spatial_dilations[2][0], spatial_dilations[2][0]),
(1, spatial_dilations[3][0], spatial_dilations[3][0]),
),
(
(1, spatial_dilations[0][1], spatial_dilations[0][1]),
(1, spatial_dilations[1][1], spatial_dilations[1][1]),
(1, spatial_dilations[1][1], spatial_dilations[1][1]),
(1, spatial_dilations[1][1], spatial_dilations[1][1]),
),
),
stage_spatial_strides=(
(
spatial_strides[0][0],
spatial_strides[1][0],
spatial_strides[2][0],
spatial_strides[3][0],
),
(
spatial_strides[0][1],
spatial_strides[1][1],
spatial_strides[2][1],
spatial_strides[3][1],
),
),
stage_temporal_strides=((1, 1, 1, 1), (1, 1, 1, 1)),
bottleneck=create_bottleneck_block,
# Head configs.
head=create_res_basic_head if not self.detection_mode else None,
head_pool=nn.AvgPool3d,
head_pool_kernel_sizes=head_pool_kernel_sizes,
head_activation=None,
head_output_with_global_average=False,
)
self.post_act = get_head_act(cfg.MODEL.HEAD_ACT)
def _construct_network(self, cfg):
"""
Builds a single pathway ResNet model.
Args:
cfg (CfgNode): model building configs, details are in the
comments of the config file.
"""
# Params from configs.
norm_module = get_norm(cfg)
head_act = get_head_act(cfg.MODEL.HEAD_ACT)
pool_size = _POOL1[cfg.MODEL.ARCH]
num_groups = cfg.RESNET.NUM_GROUPS
spatial_dilations = cfg.RESNET.SPATIAL_DILATIONS
spatial_strides = cfg.RESNET.SPATIAL_STRIDES
temp_kernel = _TEMPORAL_KERNEL_BASIS[cfg.MODEL.ARCH]
stage1_pool = pool_size[0][0] != 1 or len(set(pool_size[0])) > 1
stage_spatial_stride = (
spatial_strides[0][0],
spatial_strides[1][0],
spatial_strides[2][0],
spatial_strides[3][0],
)
if cfg.MODEL.ARCH == "i3d":
stage_conv_a_kernel_size = (
(3, 1, 1),
[(3, 1, 1), (1, 1, 1)],
[(3, 1, 1), (1, 1, 1)],
[(1, 1, 1), (3, 1, 1)],
)
else:
stage_conv_a_kernel_size = (
(temp_kernel[1][0][0], 1, 1),
(temp_kernel[2][0][0], 1, 1),
(temp_kernel[3][0][0], 1, 1),
(temp_kernel[4][0][0], 1, 1),
)
# Head from config
if cfg.DETECTION.ENABLE:
self.detection_head = create_res_roi_pooling_head(
in_features=cfg.RESNET.WIDTH_PER_GROUP * 2 ** (4 + 1),
out_features=cfg.MODEL.NUM_CLASSES,
pool=nn.AvgPool3d,
output_size=(1, 1, 1),
pool_kernel_size=(
cfg.DATA.NUM_FRAMES // pool_size[0][0],
1,
1,
),
dropout_rate=cfg.MODEL.DROPOUT_RATE,
activation=None,
output_with_global_average=False,
pool_spatial=nn.MaxPool2d,
resolution=[cfg.DETECTION.ROI_XFORM_RESOLUTION] * 2,
spatial_scale=1.0 / float(cfg.DETECTION.SPATIAL_SCALE_FACTOR),
sampling_ratio=0,
roi=ROIAlign,
)
self.model = create_resnet(
# Input clip configs.
input_channel=cfg.DATA.INPUT_CHANNEL_NUM[0],
# Model configs.
model_depth=cfg.RESNET.DEPTH,
model_num_class=cfg.MODEL.NUM_CLASSES,
dropout_rate=cfg.MODEL.DROPOUT_RATE,
# Normalization configs.
norm=norm_module,
# Activation configs.
activation=partial(nn.ReLU, inplace=cfg.RESNET.INPLACE_RELU),
# Stem configs.
stem_dim_out=cfg.RESNET.WIDTH_PER_GROUP,
stem_conv_kernel_size=(temp_kernel[0][0][0], 7, 7),
stem_conv_stride=(1, 2, 2),
stem_pool=nn.MaxPool3d,
stem_pool_kernel_size=(1, 3, 3),
stem_pool_stride=(1, 2, 2),
# Stage configs.
stage1_pool=nn.MaxPool3d if stage1_pool else None,
stage1_pool_kernel_size=pool_size[0],
stage_conv_a_kernel_size=stage_conv_a_kernel_size,
stage_conv_b_kernel_size=(
(1, 3, 3),
(1, 3, 3),
(1, 3, 3),
(1, 3, 3),
),
stage_conv_b_num_groups=(
num_groups,
num_groups,
num_groups,
num_groups,
),
stage_conv_b_dilation=(
(1, spatial_dilations[0][0], spatial_dilations[0][0]),
(1, spatial_dilations[1][0], spatial_dilations[1][0]),
(1, spatial_dilations[2][0], spatial_dilations[2][0]),
(1, spatial_dilations[3][0], spatial_dilations[3][0]),
),
stage_spatial_h_stride=stage_spatial_stride,
stage_spatial_w_stride=stage_spatial_stride,
stage_temporal_stride=(1, 1, 1, 1),
bottleneck=create_bottleneck_block,
# Head configs.
head=create_res_basic_head if not self.detection_mode else None,
head_pool=nn.AvgPool3d,
head_pool_kernel_size=(
cfg.DATA.NUM_FRAMES // pool_size[0][0],
cfg.DATA.TRAIN_CROP_SIZE // 32 // pool_size[0][1],
cfg.DATA.TRAIN_CROP_SIZE // 32 // pool_size[0][2],
),
head_activation=None,
head_output_with_global_average=False,
)
self.post_act = head_act
def test_build_head_with_callable(self):
"""
Test builder `create_res_roi_pooling_head`.
"""
# ROI layer configs
resolution = (10, 15)
spatial_scale = 1.0 / 5.0
sampling_ratio = 0
roi_layer = RoIAlign(resolution,
spatial_scale=spatial_scale,
sampling_ratio=sampling_ratio)
for (pool, activation) in itertools.product(
(nn.AvgPool3d, nn.MaxPool3d, nn.AdaptiveAvgPool3d, None),
(nn.ReLU, nn.Softmax, nn.Sigmoid, None),
):
if activation is None:
activation_model = None
elif activation == nn.Softmax:
activation_model = activation(dim=1)
else:
activation_model = activation()
if pool is None:
pool_model = None
elif pool == nn.AdaptiveAvgPool3d:
pool_model = pool(1)
else:
pool_model = pool(kernel_size=[5, 1, 1], stride=[1, 1, 1])
model = create_res_roi_pooling_head(
in_features=16,
out_features=32,
resolution=resolution,
spatial_scale=spatial_scale,
sampling_ratio=sampling_ratio,
roi=RoIAlign,
pool=pool,
pool_spatial=nn.MaxPool2d,
pool_kernel_size=(5, 1, 1),
output_size=(1, 1, 1),
dropout_rate=0.0,
activation=activation,
output_with_global_average=True,
)
model_gt = ResNetRoIHead(
proj=nn.Linear(16, 32),
activation=activation_model,
pool=pool_model,
pool_spatial=nn.MaxPool2d(resolution, stride=1),
roi_layer=roi_layer,
dropout=None,
output_pool=nn.AdaptiveAvgPool3d(1),
)
model.load_state_dict(model_gt.state_dict(),
strict=True) # explicitly use strict mode.
# Test forwarding.
for (input_tensor,
bboxes) in TestRoIHeadHelper._get_inputs(input_dim=16):
with torch.no_grad():
if (input_tensor.shape[1] != 16 or (pool is None)
or (input_tensor.shape[-3] != 5
and pool != nn.AdaptiveAvgPool3d)):
with self.assertRaises(Exception):
output_tensor = model(input_tensor, bboxes)
continue
else:
output_tensor = model(input_tensor, bboxes)
output_tensor_gt = model_gt(input_tensor, bboxes)
self.assertEqual(
output_tensor.shape,
output_tensor_gt.shape,
"Output shape {} is different from expected shape {}".
format(output_tensor.shape, output_tensor_gt.shape),
)
self.assertTrue(
np.allclose(output_tensor.numpy(),
output_tensor_gt.numpy()))
