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# Licensed under the MIT License.
import torch
from torch import nn
import torch.nn.functional as F
from .common_model import CompressionModel
from ..layers.layers import DepthConvBlock, ResidualBlockUpsample, ResidualBlockWithStride2
from ..layers.cuda_inference import CUSTOMIZED_CUDA_INFERENCE, round_and_to_int8
g_ch_src = 3 * 8 * 8
g_ch_enc_dec = 368
class IntraEncoder(nn.Module):
def __init__(self, N):
super().__init__()
self.enc_1 = DepthConvBlock(g_ch_src, g_ch_enc_dec)
self.enc_2 = nn.Sequential(
DepthConvBlock(g_ch_enc_dec, g_ch_enc_dec),
DepthConvBlock(g_ch_enc_dec, g_ch_enc_dec),
DepthConvBlock(g_ch_enc_dec, g_ch_enc_dec),
DepthConvBlock(g_ch_enc_dec, g_ch_enc_dec),
DepthConvBlock(g_ch_enc_dec, g_ch_enc_dec),
DepthConvBlock(g_ch_enc_dec, g_ch_enc_dec),
nn.Conv2d(g_ch_enc_dec, N, 3, stride=2, padding=1),
)
def forward(self, x, quant_step):
out = F.pixel_unshuffle(x, 8)
if not CUSTOMIZED_CUDA_INFERENCE or not x.is_cuda:
return self.forward_torch(out, quant_step)
return self.forward_cuda(out, quant_step)
def forward_torch(self, out, quant_step):
out = self.enc_1(out)
out = out * quant_step
return self.enc_2(out)
def forward_cuda(self, out, quant_step):
out = self.enc_1(out, quant_step=quant_step)
return self.enc_2(out)
class IntraDecoder(nn.Module):
def __init__(self, N):
super().__init__()
self.dec_1 = nn.Sequential(
ResidualBlockUpsample(N, g_ch_enc_dec),
DepthConvBlock(g_ch_enc_dec, g_ch_enc_dec),
DepthConvBlock(g_ch_enc_dec, g_ch_enc_dec),
DepthConvBlock(g_ch_enc_dec, g_ch_enc_dec),
DepthConvBlock(g_ch_enc_dec, g_ch_enc_dec),
DepthConvBlock(g_ch_enc_dec, g_ch_enc_dec),
DepthConvBlock(g_ch_enc_dec, g_ch_enc_dec),
DepthConvBlock(g_ch_enc_dec, g_ch_enc_dec),
DepthConvBlock(g_ch_enc_dec, g_ch_enc_dec),
DepthConvBlock(g_ch_enc_dec, g_ch_enc_dec),
DepthConvBlock(g_ch_enc_dec, g_ch_enc_dec),
DepthConvBlock(g_ch_enc_dec, g_ch_enc_dec),
DepthConvBlock(g_ch_enc_dec, g_ch_enc_dec),
)
self.dec_2 = DepthConvBlock(g_ch_enc_dec, g_ch_src)
def forward(self, x, quant_step):
if not CUSTOMIZED_CUDA_INFERENCE or not x.is_cuda:
return self.forward_torch(x, quant_step)
return self.forward_cuda(x, quant_step)
def forward_torch(self, x, quant_step):
out = self.dec_1(x)
out = out * quant_step
out = self.dec_2(out)
out = F.pixel_shuffle(out, 8)
return out
def forward_cuda(self, x, quant_step):
out = self.dec_1[0](x)
out = self.dec_1[1](out)
out = self.dec_1[2](out)
out = self.dec_1[3](out)
out = self.dec_1[4](out)
out = self.dec_1[5](out)
out = self.dec_1[6](out)
out = self.dec_1[7](out)
out = self.dec_1[8](out)
out = self.dec_1[9](out)
out = self.dec_1[10](out)
out = self.dec_1[11](out)
out = self.dec_1[12](out, quant_step=quant_step)
out = self.dec_2(out)
out = F.pixel_shuffle(out, 8)
return out
class DMCI(CompressionModel):
def __init__(self, N=256, z_channel=128):
super().__init__(z_channel=z_channel)
self.enc = IntraEncoder(N)
self.hyper_enc = nn.Sequential(
DepthConvBlock(N, z_channel),
ResidualBlockWithStride2(z_channel, z_channel),
ResidualBlockWithStride2(z_channel, z_channel),
)
self.hyper_dec = nn.Sequential(
ResidualBlockUpsample(z_channel, z_channel),
ResidualBlockUpsample(z_channel, z_channel),
DepthConvBlock(z_channel, N),
)
self.y_prior_fusion = nn.Sequential(
DepthConvBlock(N, N * 2),
DepthConvBlock(N * 2, N * 2),
DepthConvBlock(N * 2, N * 2),
nn.Conv2d(N * 2, N * 2 + 2, 1),
)
self.y_spatial_prior_reduction = nn.Conv2d(N * 2 + 2, N * 1, 1)
self.y_spatial_prior_adaptor_1 = DepthConvBlock(N * 2, N * 2, force_adaptor=True)
self.y_spatial_prior_adaptor_2 = DepthConvBlock(N * 2, N * 2, force_adaptor=True)
self.y_spatial_prior_adaptor_3 = DepthConvBlock(N * 2, N * 2, force_adaptor=True)
self.y_spatial_prior = nn.Sequential(
DepthConvBlock(N * 2, N * 2),
DepthConvBlock(N * 2, N * 2),
DepthConvBlock(N * 2, N * 2),
nn.Conv2d(N * 2, N * 2, 1),
)
self.dec = IntraDecoder(N)
self.q_scale_enc = nn.Parameter(torch.ones((self.get_qp_num(), g_ch_enc_dec, 1, 1)))
self.q_scale_dec = nn.Parameter(torch.ones((self.get_qp_num(), g_ch_enc_dec, 1, 1)))
def compress(self, x, qp):
device = x.device
curr_q_enc = self.q_scale_enc[qp:qp+1, :, :, :]
curr_q_dec = self.q_scale_dec[qp:qp+1, :, :, :]
y = self.enc(x, curr_q_enc)
y_pad = self.pad_for_y(y)
z = self.hyper_enc(y_pad)
z_hat, z_hat_write = round_and_to_int8(z)
params = self.hyper_dec(z_hat)
params = self.y_prior_fusion(params)
_, _, yH, yW = y.shape
params = params[:, :, :yH, :yW].contiguous()
y_q_w_0, y_q_w_1, y_q_w_2, y_q_w_3, s_w_0, s_w_1, s_w_2, s_w_3, y_hat = \
self.compress_prior_4x(
y, params, self.y_spatial_prior_reduction,
self.y_spatial_prior_adaptor_1, self.y_spatial_prior_adaptor_2,
self.y_spatial_prior_adaptor_3, self.y_spatial_prior)
cuda_event = torch.cuda.Event()
cuda_event.record()
x_hat = self.dec(y_hat, curr_q_dec).clamp_(0, 1)
cuda_stream = self.get_cuda_stream(device=device, priority=-1)
with torch.cuda.stream(cuda_stream):
cuda_event.wait()
self.entropy_coder.reset()
self.bit_estimator_z.encode_z(z_hat_write, qp)
self.gaussian_encoder.encode_y(y_q_w_0, s_w_0)
self.gaussian_encoder.encode_y(y_q_w_1, s_w_1)
self.gaussian_encoder.encode_y(y_q_w_2, s_w_2)
self.gaussian_encoder.encode_y(y_q_w_3, s_w_3)
self.entropy_coder.flush()
bit_stream = self.entropy_coder.get_encoded_stream()
torch.cuda.synchronize(device=device)
result = {
"bit_stream": bit_stream,
"x_hat": x_hat,
}
return result
def decompress(self, bit_stream, sps, qp):
dtype = next(self.parameters()).dtype
device = next(self.parameters()).device
curr_q_dec = self.q_scale_dec[qp:qp+1, :, :, :]
self.entropy_coder.set_use_two_entropy_coders(sps['ec_part'] == 1)
self.entropy_coder.set_stream(bit_stream)
z_size = self.get_downsampled_shape(sps['height'], sps['width'], 64)
y_height, y_width = self.get_downsampled_shape(sps['height'], sps['width'], 16)
self.bit_estimator_z.decode_z(z_size, qp)
z_q = self.bit_estimator_z.get_z(z_size, device, dtype)
z_hat = z_q
params = self.hyper_dec(z_hat)
params = self.y_prior_fusion(params)
params = params[:, :, :y_height, :y_width].contiguous()
y_hat = self.decompress_prior_4x(params, self.y_spatial_prior_reduction,
self.y_spatial_prior_adaptor_1,
self.y_spatial_prior_adaptor_2,
self.y_spatial_prior_adaptor_3, self.y_spatial_prior)
x_hat = self.dec(y_hat, curr_q_dec).clamp_(0, 1)
return {"x_hat": x_hat}
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