Datasets:
jblitzar
/
github-python-corpus

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parser.add_argument("--model", type=str) # model path
parser.add_argument("--data_file", type=str, default='') # data path
parser.add_argument("--start", type=int, default=0) #start index
parser.add_argument("--end", type=int, default=MAX_INT) # end index
parser.add_argument("--batch_size", type=int, default=400) # batch_size
parser.add_argument("--tensor_parallel_size", type=int, default=8) # tensor_parallel_size
parser.add_argument("--filepath_output", type=str, default=None)
return parser.parse_args()
if __name__ == "__main__":
args = parse_args()
gsm8k_test(
model=args.model,
data_path=args.data_file,
start=args.start,
end=args.end,
batch_size=args.batch_size,
tensor_parallel_size=args.tensor_parallel_size,
filepath_output=args.filepath_output
)
# <FILESEP>
import os
from PIL import Image
import numpy as np
import h5py
import cv2
def load_data(img_path,train = True):
img_folder = os.path.dirname(img_path)
img_name = os.path.basename(img_path)
index = int(img_name.split('.')[0])
prev_index = int(max(1,index-5))
post_index = int(min(150,index+5))
prev_img_path = os.path.join(img_folder,'%03d.jpg'%(prev_index))
post_img_path = os.path.join(img_folder,'%03d.jpg'%(post_index))
prev_gt_path = prev_img_path.replace('.jpg','_resize.h5')
gt_path = img_path.replace('.jpg','_resize.h5')
post_gt_path = post_img_path.replace('.jpg','_resize.h5')
prev_img = Image.open(prev_img_path).convert('RGB')
img = Image.open(img_path).convert('RGB')
post_img = Image.open(post_img_path).convert('RGB')
prev_img = prev_img.resize((640,360))
img = img.resize((640,360))
post_img = post_img.resize((640,360))
gt_file = h5py.File(gt_path)
target = np.asarray(gt_file['density'])
gt_file.close()
target = cv2.resize(target,(int(target.shape[1]/8),int(target.shape[0]/8)),interpolation = cv2.INTER_CUBIC)*64
prev_gt_file = h5py.File(prev_gt_path)
prev_target = np.asarray(prev_gt_file['density'])
prev_gt_file.close()
prev_target = cv2.resize(prev_target,(int(prev_target.shape[1]/8),int(prev_target.shape[0]/8)),interpolation = cv2.INTER_CUBIC)*64
post_gt_file = h5py.File(post_gt_path)
post_target = np.asarray(post_gt_file['density'])
post_gt_file.close()
post_target = cv2.resize(post_target,(int(post_target.shape[1]/8),int(post_target.shape[0]/8)),interpolation = cv2.INTER_CUBIC)*64
return prev_img,img,post_img,prev_target, target, post_target
# <FILESEP>
from model import EDSR
import scipy.misc
import argparse
import data
import os
parser = argparse.ArgumentParser()
parser.add_argument("--dataset",default="data/General-100")
parser.add_argument("--imgsize",default=100,type=int)
parser.add_argument("--scale",default=2,type=int)
parser.add_argument("--layers",default=32,type=int)
parser.add_argument("--featuresize",default=256,type=int)
parser.add_argument("--batchsize",default=10,type=int)
parser.add_argument("--savedir",default="saved_models")
parser.add_argument("--iterations",default=1000,type=int)
parser.add_argument("--numimgs",default=5,type=int)
parser.add_argument("--outdir",default="out")
parser.add_argument("--image")
args = parser.parse_args()
if not os.path.exists(args.outdir):
os.mkdir(args.outdir)
down_size = args.imgsize//args.scale
network = EDSR(down_size,args.layers,args.featuresize,scale=args.scale)
network.resume(args.savedir)
if args.image:
x = scipy.misc.imread(args.image)
else:
print("No image argument given")
inputs = x