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Upgrade to Cog version 0.1 - pydantic #120

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247 changes: 158 additions & 89 deletions predict.py
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Original file line number Diff line number Diff line change
@@ -1,103 +1,170 @@
import cog
import os
import tempfile
from pathlib import Path
import argparse
import shutil
import os
import cv2
import glob
import torch
from collections import OrderedDict
import numpy as np
from cog import BasePredictor, Input, Path
from main_test_swinir import define_model, setup, get_image_pair


class Predictor(cog.Predictor):
class Predictor(BasePredictor):
def setup(self):
model_dir = 'experiments/pretrained_models'

model_dir = "experiments/pretrained_models"
self.model_zoo = {
'real_sr': {
4: os.path.join(model_dir, '003_realSR_BSRGAN_DFO_s64w8_SwinIR-M_x4_GAN.pth')
"real_sr": {
"medium": os.path.join(
model_dir, "003_realSR_BSRGAN_DFO_s64w8_SwinIR-M_x4_GAN.pth"
),
"large": os.path.join(
model_dir, "003_realSR_BSRGAN_DFOWMFC_s64w8_SwinIR-L_x4_GAN.pth"
),
},
'gray_dn': {
15: os.path.join(model_dir, '004_grayDN_DFWB_s128w8_SwinIR-M_noise15.pth'),
25: os.path.join(model_dir, '004_grayDN_DFWB_s128w8_SwinIR-M_noise25.pth'),
50: os.path.join(model_dir, '004_grayDN_DFWB_s128w8_SwinIR-M_noise50.pth')
"gray_dn": {
15: os.path.join(
model_dir, "004_grayDN_DFWB_s128w8_SwinIR-M_noise15.pth"
),
25: os.path.join(
model_dir, "004_grayDN_DFWB_s128w8_SwinIR-M_noise25.pth"
),
50: os.path.join(
model_dir, "004_grayDN_DFWB_s128w8_SwinIR-M_noise50.pth"
),
},
'color_dn': {
15: os.path.join(model_dir, '005_colorDN_DFWB_s128w8_SwinIR-M_noise15.pth'),
25: os.path.join(model_dir, '005_colorDN_DFWB_s128w8_SwinIR-M_noise25.pth'),
50: os.path.join(model_dir, '005_colorDN_DFWB_s128w8_SwinIR-M_noise50.pth')
"color_dn": {
15: os.path.join(
model_dir, "005_colorDN_DFWB_s128w8_SwinIR-M_noise15.pth"
),
25: os.path.join(
model_dir, "005_colorDN_DFWB_s128w8_SwinIR-M_noise25.pth"
),
50: os.path.join(
model_dir, "005_colorDN_DFWB_s128w8_SwinIR-M_noise50.pth"
),
},
"jpeg_car": {
10: os.path.join(model_dir, "006_CAR_DFWB_s126w7_SwinIR-M_jpeg10.pth"),
20: os.path.join(model_dir, "006_CAR_DFWB_s126w7_SwinIR-M_jpeg20.pth"),
30: os.path.join(model_dir, "006_CAR_DFWB_s126w7_SwinIR-M_jpeg30.pth"),
40: os.path.join(model_dir, "006_CAR_DFWB_s126w7_SwinIR-M_jpeg40.pth"),
},
'jpeg_car': {
10: os.path.join(model_dir, '006_CAR_DFWB_s126w7_SwinIR-M_jpeg10.pth'),
20: os.path.join(model_dir, '006_CAR_DFWB_s126w7_SwinIR-M_jpeg20.pth'),
30: os.path.join(model_dir, '006_CAR_DFWB_s126w7_SwinIR-M_jpeg30.pth'),
40: os.path.join(model_dir, '006_CAR_DFWB_s126w7_SwinIR-M_jpeg40.pth')
}
}

parser = argparse.ArgumentParser()
parser.add_argument('--task', type=str, default='real_sr', help='classical_sr, lightweight_sr, real_sr, '
'gray_dn, color_dn, jpeg_car')
parser.add_argument('--scale', type=int, default=1, help='scale factor: 1, 2, 3, 4, 8') # 1 for dn and jpeg car
parser.add_argument('--noise', type=int, default=15, help='noise level: 15, 25, 50')
parser.add_argument('--jpeg', type=int, default=40, help='scale factor: 10, 20, 30, 40')
parser.add_argument('--training_patch_size', type=int, default=128, help='patch size used in training SwinIR. '
'Just used to differentiate two different settings in Table 2 of the paper. '
'Images are NOT tested patch by patch.')
parser.add_argument('--large_model', action='store_true',
help='use large model, only provided for real image sr')
parser.add_argument('--model_path', type=str,
default=self.model_zoo['real_sr'][4])
parser.add_argument('--folder_lq', type=str, default=None, help='input low-quality test image folder')
parser.add_argument('--folder_gt', type=str, default=None, help='input ground-truth test image folder')

self.args = parser.parse_args('')

self.device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
parser.add_argument(
"--task",
type=str,
default="real_sr",
help="classical_sr, lightweight_sr, real_sr, gray_dn, color_dn, jpeg_car",
)
parser.add_argument(
"--scale", type=int, default=1, help="scale factor: 1, 2, 3, 4, 8"
) # 1 for dn and jpeg car
parser.add_argument(
"--noise", type=int, default=15, help="noise level: 15, 25, 50"
)
parser.add_argument(
"--jpeg", type=int, default=40, help="scale factor: 10, 20, 30, 40"
)
parser.add_argument(
"--training_patch_size",
type=int,
default=128,
help="patch size used in training SwinIR. "
"Just used to differentiate two different settings in Table 2 of the paper. "
"Images are NOT tested patch by patch.",
)
parser.add_argument(
"--large_model",
action="store_true",
help="use large model, only provided for real image sr",
)
parser.add_argument(
"--model_path", type=str, default=self.model_zoo["real_sr"]["large"]
)
parser.add_argument(
"--folder_lq",
type=str,
default=None,
help="input low-quality test image folder",
)
parser.add_argument(
"--folder_gt",
type=str,
default=None,
help="input ground-truth test image folder",
)

self.args = parser.parse_args("")

self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

self.tasks = {
'Real-World Image Super-Resolution': 'real_sr',
'Grayscale Image Denoising': 'gray_dn',
'Color Image Denoising': 'color_dn',
'JPEG Compression Artifact Reduction': 'jpeg_car'
"Real-World Image Super-Resolution-Medium": "real_sr",
"Real-World Image Super-Resolution-Large": "real_sr",
"Grayscale Image Denoising": "gray_dn",
"Color Image Denoising": "color_dn",
"JPEG Compression Artifact Reduction": "jpeg_car",
}

@cog.input("image", type=Path, help="input image")
@cog.input("task_type", type=str, default='Real-World Image Super-Resolution',
options=['Real-World Image Super-Resolution', 'Grayscale Image Denoising', 'Color Image Denoising',
'JPEG Compression Artifact Reduction'],
help="image restoration task type")
@cog.input("noise", type=int, default=15, options=[15, 25, 50],
help='noise level, activated for Grayscale Image Denoising and Color Image Denoising. '
'Leave it as default or arbitrary if other tasks are selected')
@cog.input("jpeg", type=int, default=40, options=[10, 20, 30, 40],
help='scale factor, activated for JPEG Compression Artifact Reduction. '
'Leave it as default or arbitrary if other tasks are selected')
def predict(self, image, task_type='Real-World Image Super-Resolution', jpeg=40, noise=15):
def predict(
self,
image: Path = Input(
description="input image",
),
task_type: str = Input(
choices=[
"Real-World Image Super-Resolution-Large",
"Real-World Image Super-Resolution-Medium",
"Grayscale Image Denoising",
"Color Image Denoising",
"JPEG Compression Artifact Reduction",
],
default="Real-World Image Super-Resolution-Large",
description="Choose a task",
),
noise: int = Input(
description="noise level, activated for Grayscale Image Denoising and Color Image Denoising. "
"Leave it as default or arbitrary if other tasks are selected",
default=15,
choices=[15, 25, 50],
),
jpeg: int = Input(
default=40,
description="scale factor, activated for JPEG Compression Artifact Reduction. "
"Leave it as default or arbitrary if other tasks are selected",
),
) -> Path:

self.args.task = self.tasks[task_type]
self.args.noise = noise
self.args.jpeg = jpeg

# set model path
if self.args.task == 'real_sr':
if self.args.task == "real_sr":
self.args.scale = 4
self.args.model_path = self.model_zoo[self.args.task][4]
elif self.args.task in ['gray_dn', 'color_dn']:
if task_type == "Real-World Image Super-Resolution-Large":
self.args.model_path = self.model_zoo["real_sr"]["large"]
self.args.large_model = True
else:
self.args.model_path = self.model_zoo["real_sr"]["medium"]
self.args.large_model = False
elif self.args.task in ["gray_dn", "color_dn"]:
self.args.model_path = self.model_zoo[self.args.task][noise]
else:
self.args.model_path = self.model_zoo[self.args.task][jpeg]

# set input folder
input_dir = "input_cog_temp"

try:
# set input folder
input_dir = 'input_cog_temp'
os.makedirs(input_dir, exist_ok=True)
input_path = os.path.join(input_dir, os.path.basename(image))
shutil.copy(str(image), input_path)
if self.args.task == 'real_sr':
if self.args.task == "real_sr":
self.args.folder_lq = input_dir
else:
self.args.folder_gt = input_dir
Expand All @@ -110,50 +177,52 @@ def predict(self, image, task_type='Real-World Image Super-Resolution', jpeg=40,
folder, save_dir, border, window_size = setup(self.args)
os.makedirs(save_dir, exist_ok=True)
test_results = OrderedDict()
test_results['psnr'] = []
test_results['ssim'] = []
test_results['psnr_y'] = []
test_results['ssim_y'] = []
test_results['psnr_b'] = []
test_results["psnr"] = []
test_results["ssim"] = []
test_results["psnr_y"] = []
test_results["ssim_y"] = []
test_results["psnr_b"] = []
# psnr, ssim, psnr_y, ssim_y, psnr_b = 0, 0, 0, 0, 0
out_path = Path(tempfile.mkdtemp()) / "out.png"

for idx, path in enumerate(sorted(glob.glob(os.path.join(folder, '*')))):
for idx, path in enumerate(sorted(glob.glob(os.path.join(folder, "*")))):
# read image
imgname, img_lq, img_gt = get_image_pair(self.args, path) # image to HWC-BGR, float32
img_lq = np.transpose(img_lq if img_lq.shape[2] == 1 else img_lq[:, :, [2, 1, 0]],
(2, 0, 1)) # HCW-BGR to CHW-RGB
img_lq = torch.from_numpy(img_lq).float().unsqueeze(0).to(self.device) # CHW-RGB to NCHW-RGB
imgname, img_lq, img_gt = get_image_pair(
self.args, path
) # image to HWC-BGR, float32
img_lq = np.transpose(
img_lq if img_lq.shape[2] == 1 else img_lq[:, :, [2, 1, 0]],
(2, 0, 1),
) # HCW-BGR to CHW-RGB
img_lq = (
torch.from_numpy(img_lq).float().unsqueeze(0).to(self.device)
) # CHW-RGB to NCHW-RGB

# inference
with torch.no_grad():
# pad input image to be a multiple of window_size
_, _, h_old, w_old = img_lq.size()
h_pad = (h_old // window_size + 1) * window_size - h_old
w_pad = (w_old // window_size + 1) * window_size - w_old
img_lq = torch.cat([img_lq, torch.flip(img_lq, [2])], 2)[:, :, :h_old + h_pad, :]
img_lq = torch.cat([img_lq, torch.flip(img_lq, [3])], 3)[:, :, :, :w_old + w_pad]
img_lq = torch.cat([img_lq, torch.flip(img_lq, [2])], 2)[
:, :, : h_old + h_pad, :
]
img_lq = torch.cat([img_lq, torch.flip(img_lq, [3])], 3)[
:, :, :, : w_old + w_pad
]
output = model(img_lq)
output = output[..., :h_old * self.args.scale, :w_old * self.args.scale]
output = output[
..., : h_old * self.args.scale, : w_old * self.args.scale
]

# save image
output = output.data.squeeze().float().cpu().clamp_(0, 1).numpy()
if output.ndim == 3:
output = np.transpose(output[[2, 1, 0], :, :], (1, 2, 0)) # CHW-RGB to HCW-BGR
output = np.transpose(
output[[2, 1, 0], :, :], (1, 2, 0)
) # CHW-RGB to HCW-BGR
output = (output * 255.0).round().astype(np.uint8) # float32 to uint8
cv2.imwrite(str(out_path), output)
finally:
clean_folder(input_dir)
shutil.rmtree(input_dir)
return out_path


def clean_folder(folder):
for filename in os.listdir(folder):
file_path = os.path.join(folder, filename)
try:
if os.path.isfile(file_path) or os.path.islink(file_path):
os.unlink(file_path)
elif os.path.isdir(file_path):
shutil.rmtree(file_path)
except Exception as e:
print('Failed to delete %s. Reason: %s' % (file_path, e))