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2327452 | 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 | import torch
import torch.nn as nn
from torch.utils.checkpoint import checkpoint
def count_parameters(model: nn.Module, detailed: bool = True) -> int:
"""Count parameters in a PyTorch model.
Args:
model: PyTorch model to count parameters for
detailed: whether to print per-layer details
Returns:
Total number of parameters in the model
"""
from prettytable import PrettyTable
total_params = 0
if detailed:
table = PrettyTable(["Layer Name", "Param Count", "Trainable"])
for name, parameter in model.named_parameters():
if "img_encoder" in name:
continue
if "vol_decoder" in name:
continue
param_count = parameter.numel()
total_params += param_count
if detailed:
table.add_row([name, param_count, parameter.requires_grad])
if detailed:
print(table)
print(f"\nTotal parameters: {total_params:,}")
print(f"Trainable parameters: {sum(p.numel() for p in model.parameters() if p.requires_grad):,}")
return total_params
class SapiensDecoder(nn.Module):
def __init__(self,
in_channels,
out_channels,
deconv_out_channels,
deconv_kernel_sizes,
conv_out_channels,
conv_kernel_sizes,
interpolate_mode='bilinear',
grad_checkpointing=False,
**kwargs):
super().__init__()
self.interpolate_mode = interpolate_mode
self.grad_checkpointing = grad_checkpointing
if deconv_out_channels:
if deconv_kernel_sizes is None or len(deconv_out_channels) != len(
deconv_kernel_sizes):
raise ValueError(
'"deconv_out_channels" and "deconv_kernel_sizes" should '
'be integer sequences with the same length. Got '
f'mismatched lengths {deconv_out_channels} and '
f'{deconv_kernel_sizes}')
self.deconv_layers = self._make_deconv_layers(
in_channels=in_channels,
layer_out_channels=deconv_out_channels,
layer_kernel_sizes=deconv_kernel_sizes,
)
in_channels = deconv_out_channels[-1]
else:
self.deconv_layers = nn.Identity()
if conv_out_channels:
if conv_kernel_sizes is None or len(conv_out_channels) != len(
conv_kernel_sizes):
raise ValueError(
'"conv_out_channels" and "conv_kernel_sizes" should '
'be integer sequences with the same length. Got '
f'mismatched lengths {conv_out_channels} and '
f'{conv_kernel_sizes}')
self.conv_layers = self._make_conv_layers(
in_channels=in_channels,
layer_out_channels=conv_out_channels,
layer_kernel_sizes=conv_kernel_sizes)
in_channels = conv_out_channels[-1]
else:
self.conv_layers = nn.Identity()
self.cls_conv = nn.Conv2d(in_channels, out_channels, kernel_size=1)
def _make_conv_layers(self, in_channels,
layer_out_channels,
layer_kernel_sizes) -> nn.Module:
"""Create convolutional layers by given parameters."""
layers = []
for out_channels, kernel_size in zip(layer_out_channels,
layer_kernel_sizes):
padding = (kernel_size - 1) // 2
cfg = dict(
in_channels=in_channels,
out_channels=out_channels,
kernel_size=kernel_size,
stride=1,
padding=padding)
layers.append(
nn.Conv2d(**cfg)
)
layers.append(nn.InstanceNorm2d(out_channels))
layers.append(nn.SiLU(inplace=True))
in_channels = out_channels
return nn.Sequential(*layers)
def _make_deconv_layers(self, in_channels: int,
layer_out_channels,
layer_kernel_sizes) -> nn.Module:
"""Create deconvolutional layers by given parameters."""
layers = []
for out_channels, kernel_size in zip(layer_out_channels,
layer_kernel_sizes):
if kernel_size == 4:
padding = 1
output_padding = 0
elif kernel_size == 3:
padding = 1
output_padding = 1
elif kernel_size == 2:
padding = 0
output_padding = 0
else:
raise ValueError(f'Unsupported kernel size {kernel_size} for'
'deconvlutional layers in '
f'{self.__class__.__name__}')
cfg = dict(
in_channels=in_channels,
out_channels=out_channels,
kernel_size=kernel_size,
stride=2,
padding=padding,
output_padding=output_padding,
bias=False)
layers.append(
nn.ConvTranspose2d(**cfg)
)
layers.append(nn.InstanceNorm2d(out_channels))
layers.append(nn.SiLU(inplace=True))
in_channels = out_channels
return nn.Sequential(*layers)
def forward(self, inputs):
if self.grad_checkpointing and self.training:
x = checkpoint(self.deconv_layers, inputs, use_reentrant=False) ## B x 768 x 512 x 384
x = checkpoint(self.conv_layers, x, use_reentrant=False) ## B x 768 x 512 x 384
else:
# inputs = self._transform_inputs(inputs) ## B x 1536 x 64 x 48
x = self.deconv_layers(inputs) ## B x 768 x 512 x 384
x = self.conv_layers(x) ## B x 768 x 512 x 384
out = self.cls_conv(x) ## B x 1 x 512 x 384
return out
if __name__ == "__main__":
device = "cuda"
network = SapiensDecoder(
in_channels=512,
out_channels=3,
deconv_out_channels=(512, 512, 512, 512), ## this will 2x at each step. so total is 8x
deconv_kernel_sizes=(4, 4, 4, 4),
conv_out_channels=(512, 512, 512, 512),
conv_kernel_sizes=(1, 1, 1, 1),
).to(device)
feat = torch.randn(2, 512, 32, 32).to(device)
with torch.no_grad():
output = network(feat)
print(output.shape) ## B x 1 x 256 x 192
count_parameters(network, detailed=True)
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