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Zero
Running
on
Zero
| import torch | |
| from einops import repeat | |
| from PIL import Image | |
| import numpy as np | |
| class ResidualDenseBlock(torch.nn.Module): | |
| def __init__(self, num_feat=64, num_grow_ch=32): | |
| super(ResidualDenseBlock, self).__init__() | |
| self.conv1 = torch.nn.Conv2d(num_feat, num_grow_ch, 3, 1, 1) | |
| self.conv2 = torch.nn.Conv2d(num_feat + num_grow_ch, num_grow_ch, 3, 1, 1) | |
| self.conv3 = torch.nn.Conv2d(num_feat + 2 * num_grow_ch, num_grow_ch, 3, 1, 1) | |
| self.conv4 = torch.nn.Conv2d(num_feat + 3 * num_grow_ch, num_grow_ch, 3, 1, 1) | |
| self.conv5 = torch.nn.Conv2d(num_feat + 4 * num_grow_ch, num_feat, 3, 1, 1) | |
| self.lrelu = torch.nn.LeakyReLU(negative_slope=0.2, inplace=True) | |
| def forward(self, x): | |
| x1 = self.lrelu(self.conv1(x)) | |
| x2 = self.lrelu(self.conv2(torch.cat((x, x1), 1))) | |
| x3 = self.lrelu(self.conv3(torch.cat((x, x1, x2), 1))) | |
| x4 = self.lrelu(self.conv4(torch.cat((x, x1, x2, x3), 1))) | |
| x5 = self.conv5(torch.cat((x, x1, x2, x3, x4), 1)) | |
| return x5 * 0.2 + x | |
| class RRDB(torch.nn.Module): | |
| def __init__(self, num_feat, num_grow_ch=32): | |
| super(RRDB, self).__init__() | |
| self.rdb1 = ResidualDenseBlock(num_feat, num_grow_ch) | |
| self.rdb2 = ResidualDenseBlock(num_feat, num_grow_ch) | |
| self.rdb3 = ResidualDenseBlock(num_feat, num_grow_ch) | |
| def forward(self, x): | |
| out = self.rdb1(x) | |
| out = self.rdb2(out) | |
| out = self.rdb3(out) | |
| return out * 0.2 + x | |
| class RRDBNet(torch.nn.Module): | |
| def __init__(self, num_in_ch=3, num_out_ch=3, num_feat=64, num_block=23, num_grow_ch=32): | |
| super(RRDBNet, self).__init__() | |
| self.conv_first = torch.nn.Conv2d(num_in_ch, num_feat, 3, 1, 1) | |
| self.body = torch.torch.nn.Sequential(*[RRDB(num_feat=num_feat, num_grow_ch=num_grow_ch) for _ in range(num_block)]) | |
| self.conv_body = torch.nn.Conv2d(num_feat, num_feat, 3, 1, 1) | |
| # upsample | |
| self.conv_up1 = torch.nn.Conv2d(num_feat, num_feat, 3, 1, 1) | |
| self.conv_up2 = torch.nn.Conv2d(num_feat, num_feat, 3, 1, 1) | |
| self.conv_hr = torch.nn.Conv2d(num_feat, num_feat, 3, 1, 1) | |
| self.conv_last = torch.nn.Conv2d(num_feat, num_out_ch, 3, 1, 1) | |
| self.lrelu = torch.nn.LeakyReLU(negative_slope=0.2, inplace=True) | |
| def forward(self, x): | |
| feat = x | |
| feat = self.conv_first(feat) | |
| body_feat = self.conv_body(self.body(feat)) | |
| feat = feat + body_feat | |
| # upsample | |
| feat = repeat(feat, "B C H W -> B C (H 2) (W 2)") | |
| feat = self.lrelu(self.conv_up1(feat)) | |
| feat = repeat(feat, "B C H W -> B C (H 2) (W 2)") | |
| feat = self.lrelu(self.conv_up2(feat)) | |
| out = self.conv_last(self.lrelu(self.conv_hr(feat))) | |
| return out | |
| class ESRGAN(torch.nn.Module): | |
| def __init__(self, model): | |
| super().__init__() | |
| self.model = model | |
| def from_pretrained(model_path): | |
| model = RRDBNet() | |
| state_dict = torch.load(model_path, map_location="cpu")["params_ema"] | |
| model.load_state_dict(state_dict) | |
| model.eval() | |
| return ESRGAN(model) | |
| def process_image(self, image): | |
| image = torch.Tensor(np.array(image, dtype=np.float32) / 255).permute(2, 0, 1) | |
| return image | |
| def process_images(self, images): | |
| images = [self.process_image(image) for image in images] | |
| images = torch.stack(images) | |
| return images | |
| def decode_images(self, images): | |
| images = (images.permute(0, 2, 3, 1) * 255).clip(0, 255).numpy().astype(np.uint8) | |
| images = [Image.fromarray(image) for image in images] | |
| return images | |
| def upscale(self, images, batch_size=4, progress_bar=lambda x:x): | |
| # Preprocess | |
| input_tensor = self.process_images(images) | |
| # Interpolate | |
| output_tensor = [] | |
| for batch_id in progress_bar(range(0, input_tensor.shape[0], batch_size)): | |
| batch_id_ = min(batch_id + batch_size, input_tensor.shape[0]) | |
| batch_input_tensor = input_tensor[batch_id: batch_id_] | |
| batch_input_tensor = batch_input_tensor.to( | |
| device=self.model.conv_first.weight.device, | |
| dtype=self.model.conv_first.weight.dtype) | |
| batch_output_tensor = self.model(batch_input_tensor) | |
| output_tensor.append(batch_output_tensor.cpu()) | |
| # Output | |
| output_tensor = torch.concat(output_tensor, dim=0) | |
| # To images | |
| output_images = self.decode_images(output_tensor) | |
| return output_images | |