import os from time import time import torch import torchvision import torch.distributed as distributed from torch import nn from tqdm import tqdm from models.switched_conv.switched_conv_hard_routing import SwitchedConvHardRouting, \ convert_conv_net_state_dict_to_switched_conv from models.vqvae.vqvae import ResBlock, Quantize from trainer.networks import register_model from utils.util import checkpoint, opt_get # Upsamples and blurs (similar to StyleGAN). Replaces ConvTranspose2D from the original paper. class UpsampleConv(nn.Module): def __init__(self, in_filters, out_filters, kernel_size, padding, cfg): super().__init__() self.conv = SwitchedConvHardRouting(in_filters, out_filters, kernel_size, breadth=cfg['breadth'], include_coupler=True, coupler_mode=cfg['mode'], coupler_dim_in=in_filters, dropout_rate=cfg['dropout'], hard_en=cfg['hard_enabled']) def forward(self, x): up = torch.nn.functional.interpolate(x, scale_factor=2) return self.conv(up) class Encoder(nn.Module): def __init__(self, in_channel, channel, n_res_block, n_res_channel, stride, cfg): super().__init__() if stride == 4: blocks = [ nn.Conv2d(in_channel, channel // 2, 5, stride=2, padding=2), nn.LeakyReLU(inplace=True), SwitchedConvHardRouting(channel // 2, channel, 5, breadth=cfg['breadth'], stride=2, include_coupler=True, coupler_mode=cfg['mode'], coupler_dim_in=channel // 2, dropout_rate=cfg['dropout'], hard_en=cfg['hard_enabled']), nn.LeakyReLU(inplace=True), SwitchedConvHardRouting(channel, channel, 3, breadth=cfg['breadth'], include_coupler=True, coupler_mode=cfg['mode'], coupler_dim_in=channel, dropout_rate=cfg['dropout'], hard_en=cfg['hard_enabled']), ] elif stride == 2: blocks = [ nn.Conv2d(in_channel, channel // 2, 5, stride=2, padding=2), nn.LeakyReLU(inplace=True), SwitchedConvHardRouting(channel // 2, channel, 3, breadth=cfg['breadth'], include_coupler=True, coupler_mode=cfg['mode'], coupler_dim_in=channel // 2, dropout_rate=cfg['dropout'], hard_en=cfg['hard_enabled']), ] for i in range(n_res_block): blocks.append(ResBlock(channel, n_res_channel)) blocks.append(nn.LeakyReLU(inplace=True)) self.blocks = nn.Sequential(*blocks) def forward(self, input): return self.blocks(input) class Decoder(nn.Module): def __init__( self, in_channel, out_channel, channel, n_res_block, n_res_channel, stride, cfg ): super().__init__() blocks = [SwitchedConvHardRouting(in_channel, channel, 3, breadth=cfg['breadth'], include_coupler=True, coupler_mode=cfg['mode'], coupler_dim_in=in_channel, dropout_rate=cfg['dropout'], hard_en=cfg['hard_enabled'])] for i in range(n_res_block): blocks.append(ResBlock(channel, n_res_channel)) blocks.append(nn.LeakyReLU(inplace=True)) if stride == 4: blocks.extend( [ UpsampleConv(channel, channel // 2, 5, padding=2, cfg=cfg), nn.LeakyReLU(inplace=True), UpsampleConv( channel // 2, out_channel, 5, padding=2, cfg=cfg ), ] ) elif stride == 2: blocks.append( UpsampleConv(channel, out_channel, 5, padding=2, cfg=cfg) ) self.blocks = nn.Sequential(*blocks) def forward(self, input): return self.blocks(input) class VQVAE3HardSwitch(nn.Module): def __init__( self, in_channel=3, channel=128, n_res_block=2, n_res_channel=32, codebook_dim=64, codebook_size=512, decay=0.99, cfg={'mode':'standard', 'breadth':16, 'hard_enabled': True, 'dropout': 0.4} ): super().__init__() self.cfg = cfg self.initial_conv = nn.Sequential(*[nn.Conv2d(in_channel, 32, 3, padding=1), nn.LeakyReLU(inplace=True)]) self.enc_b = Encoder(32, channel, n_res_block, n_res_channel, stride=4, cfg=cfg) self.enc_t = Encoder(channel, channel, n_res_block, n_res_channel, stride=2, cfg=cfg) self.quantize_conv_t = nn.Conv2d(channel, codebook_dim, 1) self.quantize_t = Quantize(codebook_dim, codebook_size) self.dec_t = Decoder( codebook_dim, codebook_dim, channel, n_res_block, n_res_channel, stride=2, cfg=cfg ) self.quantize_conv_b = nn.Conv2d(codebook_dim + channel, codebook_dim, 1) self.quantize_b = Quantize(codebook_dim, codebook_size) self.upsample_t = UpsampleConv( codebook_dim, codebook_dim, 5, padding=2, cfg=cfg ) self.dec = Decoder( codebook_dim + codebook_dim, 32, channel, n_res_block, n_res_channel, stride=4, cfg=cfg ) self.final_conv = nn.Conv2d(32, in_channel, 3, padding=1) def forward(self, input): quant_t, quant_b, diff, _, _ = self.encode(input) dec = self.decode(quant_t, quant_b) return dec, diff def save_attention_to_image_rgb(self, output_file, attention_out, attention_size, cmap_discrete_name='viridis'): from matplotlib import cm magnitude, indices = torch.topk(attention_out, 3, dim=1) indices = indices.cpu() colormap = cm.get_cmap(cmap_discrete_name, attention_size) img = torch.tensor(colormap(indices[:, 0, :, :].detach().numpy())) # TODO: use other k's img = img.permute((0, 3, 1, 2)) torchvision.utils.save_image(img, output_file) def visual_dbg(self, step, path): convs = [self.dec.blocks[-1].conv, self.dec_t.blocks[-1].conv, self.enc_b.blocks[-4], self.enc_t.blocks[-4]] for i, c in enumerate(convs): self.save_attention_to_image_rgb(os.path.join(path, "%i_selector_%i.png" % (step, i+1)), c.last_select, 16) def get_debug_values(self, step, __): switched_convs = [('enc_b_blk2', self.enc_b.blocks[2]), ('enc_b_blk4', self.enc_b.blocks[4]), ('enc_t_blk2', self.enc_t.blocks[2]), ('dec_t_blk0', self.dec_t.blocks[0]), ('dec_t_blk-1', self.dec_t.blocks[-1].conv), ('dec_blk0', self.dec.blocks[0]), ('dec_blk-1', self.dec.blocks[-1].conv), ('dec_blk-3', self.dec.blocks[-3].conv)] logs = {} for name, swc in switched_convs: logs[f'{name}_histogram_switch_usage'] = swc.latest_masks return logs def encode(self, input): fea = self.initial_conv(input) enc_b = checkpoint(self.enc_b, fea) enc_t = checkpoint(self.enc_t, enc_b) quant_t = self.quantize_conv_t(enc_t).permute(0, 2, 3, 1) quant_t, diff_t, id_t = self.quantize_t(quant_t) quant_t = quant_t.permute(0, 3, 1, 2) diff_t = diff_t.unsqueeze(0) dec_t = checkpoint(self.dec_t, quant_t) enc_b = torch.cat([dec_t, enc_b], 1) quant_b = checkpoint(self.quantize_conv_b, enc_b).permute(0, 2, 3, 1) quant_b, diff_b, id_b = self.quantize_b(quant_b) quant_b = quant_b.permute(0, 3, 1, 2) diff_b = diff_b.unsqueeze(0) return quant_t, quant_b, diff_t + diff_b, id_t, id_b def decode(self, quant_t, quant_b): upsample_t = self.upsample_t(quant_t) quant = torch.cat([upsample_t, quant_b], 1) dec = checkpoint(self.dec, quant) dec = checkpoint(self.final_conv, dec) return dec def decode_code(self, code_t, code_b): quant_t = self.quantize_t.embed_code(code_t) quant_t = quant_t.permute(0, 3, 1, 2) quant_b = self.quantize_b.embed_code(code_b) quant_b = quant_b.permute(0, 3, 1, 2) dec = self.decode(quant_t, quant_b) return dec def convert_weights(weights_file): sd = torch.load(weights_file) from models.vqvae.vqvae_3 import VQVAE3 std_model = VQVAE3() std_model.load_state_dict(sd) nsd = convert_conv_net_state_dict_to_switched_conv(std_model, 16, ['quantize_conv_t', 'quantize_conv_b', 'enc_b.blocks.0', 'enc_t.blocks.0', 'conv.1', 'conv.3', 'initial_conv', 'final_conv']) torch.save(nsd, "converted.pth") @register_model def register_vqvae3_hard_switch(opt_net, opt): kw = opt_get(opt_net, ['kwargs'], {}) vq = VQVAE3HardSwitch(**kw) if distributed.is_initialized() and distributed.get_world_size() > 1: vq = torch.nn.SyncBatchNorm.convert_sync_batchnorm(vq) return vq def performance_test(): # For breadth=32: # Custom_cuda_naive: 28.9s # Torch_native: 29.2s # # For breadth=8 # Custom_cuda_naive: 18.4s # Torch_native: 10s cfg = { 'mode': 'lambda', 'breadth': 8, 'hard_enabled': True, 'dropout': 0.4 } net = VQVAE3HardSwitch(cfg=cfg).to('cuda') loss = nn.L1Loss() opt = torch.optim.Adam(net.parameters(), lr=1e-4) started = time() for j in tqdm(range(10)): inp = torch.rand((8, 3, 256, 256), device='cuda') res = net(inp)[0] l = loss(res, inp) l.backward() opt.step() net.zero_grad() print("Elapsed: ", (time()-started)) if __name__ == '__main__': #v = VQVAE3HardSwitch() #print(v(torch.randn(1,3,128,128))[0].shape) #convert_weights("../../../experiments/vqvae_base.pth") performance_test()