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# Licensed under the MIT License.
import torch
from torch import nn
from ..layers.cuda_inference import combine_for_reading_2x, \
restore_y_2x, restore_y_2x_with_cat_after, add_and_multiply, \
replicate_pad, restore_y_4x, clamp_reciprocal_with_quant
from .entropy_models import BitEstimator, GaussianEncoder, EntropyCoder
class CompressionModel(nn.Module):
def __init__(self, z_channel, extra_qp=0):
super().__init__()
self.z_channel = z_channel
self.entropy_coder = None
self.bit_estimator_z = BitEstimator(64 + extra_qp, z_channel)
self.gaussian_encoder = GaussianEncoder()
self.masks = {}
self.cuda_streams = {}
def get_cuda_stream(self, device, idx=0, priority=0):
key = f"{device}_{priority}_{idx}"
if key not in self.cuda_streams:
self.cuda_streams[key] = torch.cuda.Stream(device, priority=priority)
return self.cuda_streams[key]
@staticmethod
def get_qp_num():
return 64
@staticmethod
def get_padding_size(height, width, p=64):
new_h = (height + p - 1) // p * p
new_w = (width + p - 1) // p * p
padding_right = new_w - width
padding_bottom = new_h - height
return padding_right, padding_bottom
@staticmethod
def get_downsampled_shape(height, width, p):
new_h = (height + p - 1) // p * p
new_w = (width + p - 1) // p * p
return int(new_h / p + 0.5), int(new_w / p + 0.5)
def update(self, force_zero_thres=None):
self.entropy_coder = EntropyCoder()
self.gaussian_encoder.update(self.entropy_coder, force_zero_thres=force_zero_thres)
self.bit_estimator_z.update(self.entropy_coder)
def set_use_two_entropy_coders(self, use_two_entropy_coders):
self.entropy_coder.set_use_two_entropy_coders(use_two_entropy_coders)
def pad_for_y(self, y):
_, _, H, W = y.size()
padding_r, padding_b = self.get_padding_size(H, W, 4)
y_pad = replicate_pad(y, padding_b, padding_r)
return y_pad
def separate_prior(self, params, is_video=False):
if is_video:
quant_step, scales, means = params.chunk(3, 1)
quant_step = torch.clamp_min(quant_step, 0.5)
q_enc = 1. / quant_step
q_dec = quant_step
else:
q = params[:, :2, :, :]
q_enc, q_dec = (torch.sigmoid(q) * 1.5 + 0.5).chunk(2, 1)
scales, means = params[:, 2:, :, :].chunk(2, 1)
return q_enc, q_dec, scales, means
@staticmethod
def separate_prior_for_video_encoding(params, y):
q_dec, scales, means = params.chunk(3, 1)
q_dec, y = clamp_reciprocal_with_quant(q_dec, y, 0.5)
return y, q_dec, scales, means
@staticmethod
def separate_prior_for_video_decoding(params):
quant_step, scales, means = params.chunk(3, 1)
quant_step = torch.clamp_min(quant_step, 0.5)
return quant_step, scales, means
def process_with_mask(self, y, scales, means, mask):
return self.gaussian_encoder.process_with_mask(y, scales, means, mask)
@staticmethod
def get_one_mask(micro_mask, height, width, dtype, device):
mask = torch.tensor(micro_mask, dtype=dtype, device=device)
mask = mask.repeat((height + 1) // 2, (width + 1) // 2)
mask = mask[:height, :width]
mask = torch.unsqueeze(mask, 0)
mask = torch.unsqueeze(mask, 0)
return mask
def get_mask_4x(self, batch, channel, height, width, dtype, device):
curr_mask_str = f"{batch}_{channel}_{width}_{height}_4x"
with torch.no_grad():
if curr_mask_str not in self.masks:
assert channel % 4 == 0
m = torch.ones((batch, channel // 4, height, width), dtype=dtype, device=device)
m0 = self.get_one_mask(((1, 0), (0, 0)), height, width, dtype, device)
m1 = self.get_one_mask(((0, 1), (0, 0)), height, width, dtype, device)
m2 = self.get_one_mask(((0, 0), (1, 0)), height, width, dtype, device)
m3 = self.get_one_mask(((0, 0), (0, 1)), height, width, dtype, device)
mask_0 = torch.cat((m * m0, m * m1, m * m2, m * m3), dim=1)
mask_1 = torch.cat((m * m3, m * m2, m * m1, m * m0), dim=1)
mask_2 = torch.cat((m * m2, m * m3, m * m0, m * m1), dim=1)
mask_3 = torch.cat((m * m1, m * m0, m * m3, m * m2), dim=1)
self.masks[curr_mask_str] = [mask_0, mask_1, mask_2, mask_3]
return self.masks[curr_mask_str]
def get_mask_2x(self, batch, channel, height, width, dtype, device):
curr_mask_str = f"{batch}_{channel}_{width}_{height}_2x"
with torch.no_grad():
if curr_mask_str not in self.masks:
assert channel % 2 == 0
m = torch.ones((batch, channel // 2, height, width), dtype=dtype, device=device)
m0 = self.get_one_mask(((1, 0), (0, 1)), height, width, dtype, device)
m1 = self.get_one_mask(((0, 1), (1, 0)), height, width, dtype, device)
mask_0 = torch.cat((m * m0, m * m1), dim=1)
mask_1 = torch.cat((m * m1, m * m0), dim=1)
self.masks[curr_mask_str] = [mask_0, mask_1]
return self.masks[curr_mask_str]
@staticmethod
def single_part_for_writing_4x(x):
x0, x1, x2, x3 = x.chunk(4, 1)
return (x0 + x1) + (x2 + x3)
@staticmethod
def single_part_for_writing_2x(x):
x0, x1 = x.chunk(2, 1)
return x0 + x1
def compress_prior_2x(self, y, common_params, y_spatial_prior):
y, q_dec, scales, means = self.separate_prior_for_video_encoding(common_params, y)
dtype = y.dtype
device = y.device
B, C, H, W = y.size()
mask_0, mask_1 = self.get_mask_2x(B, C, H, W, dtype, device)
_, y_q_0, y_hat_0, s_hat_0 = self.process_with_mask(y, scales, means, mask_0)
cat_params = torch.cat((y_hat_0, common_params), dim=1)
scales, means = y_spatial_prior(cat_params).chunk(2, 1)
_, y_q_1, y_hat_1, s_hat_1 = self.process_with_mask(y, scales, means, mask_1)
y_hat = add_and_multiply(y_hat_0, y_hat_1, q_dec)
y_q_w_0 = self.single_part_for_writing_2x(y_q_0)
y_q_w_1 = self.single_part_for_writing_2x(y_q_1)
s_w_0 = self.single_part_for_writing_2x(s_hat_0)
s_w_1 = self.single_part_for_writing_2x(s_hat_1)
return y_q_w_0, y_q_w_1, s_w_0, s_w_1, y_hat
def decompress_prior_2x(self, common_params, y_spatial_prior):
infos = self.decompress_prior_2x_part1(common_params)
y_hat = self.decompress_prior_2x_part2(common_params, y_spatial_prior, infos)
return y_hat
def decompress_prior_2x_part1(self, common_params):
q_dec, scales, means = self.separate_prior_for_video_decoding(common_params)
dtype = means.dtype
device = means.device
B, C, H, W = means.size()
mask_0, mask_1 = self.get_mask_2x(B, C, H, W, dtype, device)
scales_r = combine_for_reading_2x(scales, mask_0, inplace=False)
indexes, skip_cond = self.gaussian_encoder.build_indexes_decoder(scales_r)
self.gaussian_encoder.decode_y(indexes)
infos = {
"q_dec": q_dec,
"mask_0": mask_0,
"mask_1": mask_1,
"means": means,
"scales_r": scales_r,
"skip_cond": skip_cond,
"indexes": indexes,
}
return infos
def decompress_prior_2x_part2(self, common_params, y_spatial_prior, infos):
dtype = common_params.dtype
device = common_params.device
y_q_r = self.gaussian_encoder.get_y(infos["scales_r"].shape,
infos["scales_r"].numel(),
dtype, device,
infos["skip_cond"], infos["indexes"])
y_hat_0, cat_params = restore_y_2x_with_cat_after(y_q_r, infos["means"], infos["mask_0"],
common_params)
scales, means = y_spatial_prior(cat_params).chunk(2, 1)
scales_r = combine_for_reading_2x(scales, infos["mask_1"], inplace=True)
y_q_r = self.gaussian_encoder.decode_and_get_y(scales_r, dtype, device)
y_hat_1 = restore_y_2x(y_q_r, means, infos["mask_1"])
y_hat = add_and_multiply(y_hat_0, y_hat_1, infos["q_dec"])
return y_hat
def compress_prior_4x(self, y, common_params, y_spatial_prior_reduction,
y_spatial_prior_adaptor_1, y_spatial_prior_adaptor_2,
y_spatial_prior_adaptor_3, y_spatial_prior):
'''
y_0 means split in channel, the 0/4 quater
y_1 means split in channel, the 1/4 quater
y_2 means split in channel, the 2/4 quater
y_3 means split in channel, the 3/4 quater
y_?_0, means multiply with mask_0
y_?_1, means multiply with mask_1
y_?_2, means multiply with mask_2
y_?_3, means multiply with mask_3
'''
q_enc, q_dec, scales, means = self.separate_prior(common_params, False)
common_params = y_spatial_prior_reduction(common_params)
dtype = y.dtype
device = y.device
B, C, H, W = y.size()
mask_0, mask_1, mask_2, mask_3 = self.get_mask_4x(B, C, H, W, dtype, device)
y = y * q_enc
_, y_q_0, y_hat_0, s_hat_0 = self.process_with_mask(y, scales, means, mask_0)
y_hat_so_far = y_hat_0
params = torch.cat((y_hat_so_far, common_params), dim=1)
scales, means = y_spatial_prior(y_spatial_prior_adaptor_1(params)).chunk(2, 1)
_, y_q_1, y_hat_1, s_hat_1 = self.process_with_mask(y, scales, means, mask_1)
y_hat_so_far = y_hat_so_far + y_hat_1
params = torch.cat((y_hat_so_far, common_params), dim=1)
scales, means = y_spatial_prior(y_spatial_prior_adaptor_2(params)).chunk(2, 1)
_, y_q_2, y_hat_2, s_hat_2 = self.process_with_mask(y, scales, means, mask_2)
y_hat_so_far = y_hat_so_far + y_hat_2
params = torch.cat((y_hat_so_far, common_params), dim=1)
scales, means = y_spatial_prior(y_spatial_prior_adaptor_3(params)).chunk(2, 1)
_, y_q_3, y_hat_3, s_hat_3 = self.process_with_mask(y, scales, means, mask_3)
y_hat = y_hat_so_far + y_hat_3
y_hat = y_hat * q_dec
y_q_w_0 = self.single_part_for_writing_4x(y_q_0)
y_q_w_1 = self.single_part_for_writing_4x(y_q_1)
y_q_w_2 = self.single_part_for_writing_4x(y_q_2)
y_q_w_3 = self.single_part_for_writing_4x(y_q_3)
s_w_0 = self.single_part_for_writing_4x(s_hat_0)
s_w_1 = self.single_part_for_writing_4x(s_hat_1)
s_w_2 = self.single_part_for_writing_4x(s_hat_2)
s_w_3 = self.single_part_for_writing_4x(s_hat_3)
return 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
def decompress_prior_4x(self, common_params, y_spatial_prior_reduction,
y_spatial_prior_adaptor_1, y_spatial_prior_adaptor_2,
y_spatial_prior_adaptor_3, y_spatial_prior):
_, quant_step, scales, means = self.separate_prior(common_params, False)
common_params = y_spatial_prior_reduction(common_params)
dtype = means.dtype
device = means.device
B, C, H, W = means.size()
mask_0, mask_1, mask_2, mask_3 = self.get_mask_4x(B, C, H, W, dtype, device)
scales_r = self.single_part_for_writing_4x(scales * mask_0)
y_q_r = self.gaussian_encoder.decode_and_get_y(scales_r, dtype, device)
y_hat_curr_step = restore_y_4x(y_q_r, means, mask_0)
y_hat_so_far = y_hat_curr_step
params = torch.cat((y_hat_so_far, common_params), dim=1)
scales, means = y_spatial_prior(y_spatial_prior_adaptor_1(params)).chunk(2, 1)
scales_r = self.single_part_for_writing_4x(scales * mask_1)
y_q_r = self.gaussian_encoder.decode_and_get_y(scales_r, dtype, device)
y_hat_curr_step = restore_y_4x(y_q_r, means, mask_1)
y_hat_so_far = y_hat_so_far + y_hat_curr_step
params = torch.cat((y_hat_so_far, common_params), dim=1)
scales, means = y_spatial_prior(y_spatial_prior_adaptor_2(params)).chunk(2, 1)
scales_r = self.single_part_for_writing_4x(scales * mask_2)
y_q_r = self.gaussian_encoder.decode_and_get_y(scales_r, dtype, device)
y_hat_curr_step = restore_y_4x(y_q_r, means, mask_2)
y_hat_so_far = y_hat_so_far + y_hat_curr_step
params = torch.cat((y_hat_so_far, common_params), dim=1)
scales, means = y_spatial_prior(y_spatial_prior_adaptor_3(params)).chunk(2, 1)
scales_r = self.single_part_for_writing_4x(scales * mask_3)
y_q_r = self.gaussian_encoder.decode_and_get_y(scales_r, dtype, device)
y_hat_curr_step = restore_y_4x(y_q_r, means, mask_3)
y_hat_so_far = y_hat_so_far + y_hat_curr_step
y_hat = y_hat_so_far * quant_step
return y_hat
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