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Upgrade to Cog version 0.1 #5

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Upgrade to Cog version 0.1 #5

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30 changes: 16 additions & 14 deletions predict.py
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Original file line number Diff line number Diff line change
@@ -1,5 +1,5 @@
import sys
from pathlib import Path
from typing import Iterator
import torch
import torch.optim as optim
from torchvision import transforms, models
Expand All @@ -11,11 +11,11 @@
from template import imagenet_templates
from torchvision.utils import save_image
from torchvision.transforms.functional import adjust_contrast
import cog
from cog import BasePredictor, Input, Path
from argparse import Namespace


class Predictor(cog.Predictor):
class Predictor(BasePredictor):
def setup(self):
self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
self.VGG = models.vgg19(pretrained=True).features
Expand All @@ -26,10 +26,12 @@ def setup(self):
self.style_net.to(self.device)
self.clip_model, preprocess = clip.load('ViT-B/32', self.device, jit=False)

@cog.input("image", type=Path, help="Input image (will be cropped before style transfer)")
@cog.input("text", type=str, help="text for style transfer")
@cog.input("iterations", type=int, default=100, help="training iterations")
def predict(self, image, text, iterations):
def predict(
self,
image: Path = Input(description="Input image (will be cropped before style transfer)"),
text: str = Input(description="text for style transfer"),
iterations: int = Input(default=100, description="training iterations")
) -> Iterator[Path]:
training_args = {
"lambda_tv": 2e-3,
"lambda_patch": 9000,
Expand Down Expand Up @@ -141,17 +143,17 @@ def predict(self, image, text, iterations):
if epoch % 20 == 0 or epoch == steps:
yield checkin(epoch, target, total_loss, content_loss, loss_patch, loss_glob, reg_tv, out_path)

return out_path
yield out_path


@torch.no_grad()
def checkin(epoch, target, total_loss, content_loss, loss_patch, loss_glob, reg_tv, out_path):
sys.stderr.write(f'After {epoch} iterations')
sys.stderr.write(f'Total loss: {total_loss.item()}')
sys.stderr.write(f'Content loss: {content_loss.item()}')
sys.stderr.write(f'patch loss: {loss_patch.item()}')
sys.stderr.write(f'dir loss: {loss_glob.item()}')
sys.stderr.write(f'TV loss: {reg_tv.item()}')
sys.stderr.write(f'After {epoch} iterations\n')
sys.stderr.write(f'Total loss: {total_loss.item()}\n')
sys.stderr.write(f'Content loss: {content_loss.item()}\n')
sys.stderr.write(f'patch loss: {loss_patch.item()}\n')
sys.stderr.write(f'dir loss: {loss_glob.item()}\n')
sys.stderr.write(f'TV loss: {reg_tv.item()}\n')
output_image = target.clone()
output_image = torch.clamp(output_image, 0, 1)
output_image = adjust_contrast(output_image, 1.5)
Expand Down