我们从Python开源项目中,提取了以下30个代码示例,用于说明如何使用caffe.Layer()。
def load_next_image(self,loss_task): if loss_task == 0: if self.cls_cur == len(self.cls_list): self.cls_cur = 0 random.shuffle(self.cls_list) cur_data = self.cls_list[self.cls_cur] # Get the image index im = cur_data[0] label = cur_data[1] roi = [-1,-1,-1,-1] pts = [-1,-1,-1,-1,-1,-1,-1,-1,-1,-1] if random.choice([0,1])==1: im = cv2.flip(im,random.choice([-1,0,1])) self.cls_cur += 1 return im, label, roi, pts if loss_task == 1: if self.roi_cur == len(self.roi_list): self.roi_cur = 0 random.shuffle(self.roi_list) cur_data = self.roi_list[self.roi_cur] # Get the image index im = cur_data[0] label = -1 roi = cur_data[2] pts = [-1,-1,-1,-1,-1,-1,-1,-1,-1,-1] self.roi_cur += 1 return im, label, roi, pts if loss_task == 2: if self.pts_cur == len(self.pts_list): self.pts_cur = 0 random.shuffle(self.pts_list) cur_data = self.pts_list[self.pts_cur] # Get the image index im = cur_data[0] label = -1 roi = [-1,-1,-1,-1] pts = cur_data[3] self.pts_cur += 1 return im, label, roi, pts ################################################################################ ######################Regression Loss Layer By Python########################### ################################################################################
def parse_args(cls, argsStr): parser = argparse.ArgumentParser(description='PythonWindowDataParallel Layer') parser.add_argument('--source', default='', type=str) parser.add_argument('--root_folder', default='', type=str) parser.add_argument('--mean_file', default='', type=str) parser.add_argument('--batch_size', default=128, type=int) parser.add_argument('--crop_size', default=192, type=int) parser.add_argument('--is_gray', dest='is_gray', action='store_true') parser.add_argument('--no-is_gray', dest='is_gray', action='store_false') parser.add_argument('--is_mirror', dest='is_mirror', action='store_true', default=False) parser.add_argument('--resume_iter', default=0, type=int) parser.add_argument('--jitter_pct', default=0, type=float) parser.add_argument('--jitter_amt', default=0, type=int) parser.add_argument('--ncpu', default=2, type=int) args = parser.parse_args(argsStr.split()) print('Using Config:') pprint.pprint(args) return args
def parse_args(cls, argsStr): parser = argparse.ArgumentParser(description='PythonWindowDataRots Layer') parser.add_argument('--source', default='', type=str) parser.add_argument('--root_folder', default='', type=str) parser.add_argument('--mean_file', default='', type=str) parser.add_argument('--batch_size', default=128, type=int) parser.add_argument('--crop_size', default=192, type=int) parser.add_argument('--is_gray', dest='is_gray', action='store_true') parser.add_argument('--no-is_gray', dest='is_gray', action='store_false') parser.add_argument('--is_random_roll', dest='is_gray', action='store_true', default=True) parser.add_argument('--no-is_random_roll', dest='is_gray', action='store_false') parser.add_argument('--is_mirror', dest='is_mirror', action='store_true', default=False) parser.add_argument('--resume_iter', default=0, type=int) parser.add_argument('--jitter_pct', default=0, type=float) parser.add_argument('--jitter_amt', default=0, type=int) parser.add_argument('--nrmlz_file', default='None', type=str) parser.add_argument('--ncpu', default=2, type=int) args = parser.parse_args(argsStr.split()) print('Using Config:') pprint.pprint(args) return args
def _parse_args(self, str_arg): parser = argparse.ArgumentParser(description='Python Layer Parameters Pi') parser.add_argument('--num_classes', default=None, type=int) parser.add_argument('--num_data', default=None, type=int) args = parser.parse_args(str_arg.split()) return args
def forward(self, bottom, top): # assign the output of the Python Layer as the input of the # next layers top[0].data[...] = self.data top[1].data[...] = self.label # pick next input randomly, if in train, or sequentially if in testing if self.random: self.idx = random.randint(0, len(self.indices)-1) else: self.idx += 1 if self.idx == len(self.indices): self.idx = 0
def forward(self, bottom, top): # assign the output of the Python Layer as the input of the # next layers for i, t in enumerate(self.tops): top[i].data[...] = self.data[t] # pick next input randomly if in train, or sequentially if in testing if self.random: self.idx = random.randint(0, len(self.indices)-1) else: self.idx += 1 if self.idx == len(self.indices): self.idx = 0
def view_bar(num, total): rate = float(num) / total rate_num = int(rate * 100) r = '\r[%s%s]%d%%' % ("#"*rate_num, " "*(100-rate_num), rate_num, ) sys.stdout.write(r) sys.stdout.flush() ################################################################################ #########################Data Layer By Python################################### ################################################################################
def backward(self,top,propagate_down,bottom): for i in range(2): if not propagate_down[i] or self.N==0: continue if i == 0: sign = 1 else: sign = -1 bottom[i].diff[...] = sign * self.diff / bottom[i].num ################################################################################ #############################Classify Layer By Python########################### ################################################################################
def load_next_image(self,loss_task): if loss_task == 0: if self.cls_cur == len(self.cls_list): self.cls_cur = 0 random.shuffle(self.cls_list) cur_data = self.cls_list[self.cls_cur] # Get the image index im = cur_data[0] label = cur_data[1] roi = [-1,-1,-1,-1] pts = [-1,-1,-1,-1,-1,-1,-1,-1,-1,-1] if random.choice([0,1])==1: im = cv2.flip(im,random.choice([-1,0,1])) self.cls_cur += 1 return im, label, roi, pts if loss_task == 1: if self.roi_cur == len(self.roi_list): self.roi_cur = 0 random.shuffle(self.roi_list) cur_data = self.roi_list[self.roi_cur] # Get the image index im = cur_data[0] label = -1 roi = cur_data[2] pts = [-1,-1,-1,-1,-1,-1,-1,-1,-1,-1] self.roi_cur += 1 return im, label, roi, pts if loss_task == 2: if self.pts_cur == len(self.pts_list): self.pts_cur = 0 random.shuffle(self.pts_list) cur_data = self.pts_list[self.pts_cur] # Get the image index im = cur_data[0] label = -1 roi = [-1,-1,-1,-1] pts = cur_data[3] self.pts_cur += 1 return im, label, roi, pts ################################################################################ #########################ROI Loss Layer By Python############################### ################################################################################
def backward(self,top,propagate_down,bottom): for i in range(2): if not propagate_down[i] or self.N==0: continue if i == 0: sign = 1 else: sign = -1 bottom[i].diff[...] = sign * self.diff / bottom[i].num ################################################################################ #############################SendData Layer By Python########################### ################################################################################
def parse_args(cls, argsStr): parser = argparse.ArgumentParser(description='PythonGroupDataRots Layer') parser.add_argument('--im_root_folder', default='', type=str) #The file which contains the name of groups parser.add_argument('--grplist_file', default='', type=str) #File containing information what kind of labels #should be extractee etc. parser.add_argument('--lbinfo_file', default='', type=str) parser.add_argument('--mean_file', default='', type=str) parser.add_argument('--batch_size', default=128, type=int) parser.add_argument('--crop_size', default=192, type=int) parser.add_argument('--im_size', default=101, type=int) parser.add_argument('--is_gray', dest='is_gray', action='store_true') parser.add_argument('--no-is_gray', dest='is_gray', action='store_false') parser.add_argument('--random_roll_max', default=0, type=float) parser.add_argument('--is_mirror', dest='is_mirror', action='store_true', default=False) parser.add_argument('--resume_iter', default=0, type=int) parser.add_argument('--jitter_pct', default=0, type=float) parser.add_argument('--jitter_amt', default=0, type=int) parser.add_argument('--nrmlz_file', default='None', type=str) parser.add_argument('--ncpu', default=2, type=int) #For debugging - load a single group parser.add_argument('--is_single_grp', dest='is_single_grp', action='store_true', default=False ) parser.add_argument('--no-is_single_grp', dest='is_single_grp', action='store_false') args = parser.parse_args(argsStr.split()) print('Using Config:') pprint.pprint(args) return args
def parse_args(cls, argsStr): parser = argparse.ArgumentParser(description='Python Window Data Layer') parser.add_argument('--source', default='', type=str) parser.add_argument('--root_folder', default='', type=str) parser.add_argument('--mean_file', default='', type=str) parser.add_argument('--batch_size', default=128, type=int) parser.add_argument('--crop_size', default=192, type=int) parser.add_argument('--is_gray', dest='is_gray', action='store_true') parser.add_argument('--no-is_gray', dest='is_gray', action='store_false') parser.add_argument('--resume_iter', default=0, type=int) args = parser.parse_args(argsStr.split()) print('Using Config:') pprint.pprint(args) return args
def parse_args(cls, argsStr): parser = argparse.ArgumentParser(description='PythonWindowDataParallel Layer') parser.add_argument('--num_threads', default=16, type=int) parser.add_argument('--source', default='', type=str) parser.add_argument('--root_folder', default='', type=str) parser.add_argument('--mean_file', default='', type=str) parser.add_argument('--batch_size', default=128, type=int) parser.add_argument('--crop_size', default=192, type=int) parser.add_argument('--is_gray', dest='is_gray', action='store_true') parser.add_argument('--no-is_gray', dest='is_gray', action='store_false') parser.add_argument('--resume_iter', default=0, type=int) args = parser.parse_args(argsStr.split()) print('Using Config:') pprint.pprint(args) return args
def parse_args(cls, argsStr): parser = argparse.ArgumentParser(description='Python L1 Loss Layer') parser.add_argument('--loss_weight', default=1.0, type=float) args = parser.parse_args(argsStr.split()) print('Using Config:') pprint.pprint(args) return args
def parse_args(cls, argsStr): parser = argparse.ArgumentParser(description='Python L1 Loss With Ignore Layer') parser.add_argument('--loss_weight', default=1.0, type=float) args = parser.parse_args(argsStr.split()) print('Using Config:') pprint.pprint(args) return args
def parse_args(cls, argsStr): parser = argparse.ArgumentParser(description='Python L2Loss With Ignore Layer') parser.add_argument('--loss_weight', default=1.0, type=float) args = parser.parse_args(argsStr.split()) print('Using Config:') pprint.pprint(args) return args
def parse_args(cls, argsStr): parser = argparse.ArgumentParser(description='Python L2LossQuaternion With Ignore Layer') parser.add_argument('--loss_weight', default=1.0, type=float) args = parser.parse_args(argsStr.split()) print('Using Config:') pprint.pprint(args) return args
def parse_args(cls, argsStr): parser = argparse.ArgumentParser(description='Python L1 Weighted Loss Layer') parser.add_argument('--loss_weight', default=1.0, type=float) args = parser.parse_args(argsStr.split()) print('Using Config:') pprint.pprint(args) return args
def parse_args(cls, argsStr): parser = argparse.ArgumentParser(description='GaussRender Layer') parser.add_argument('--K', default=100.0, type=float) parser.add_argument('--T', default=-50.0, type=float) parser.add_argument('--sigma', default=0.001, type=float) parser.add_argument('--imgSz', default=224, type=int) args = parser.parse_args(argsStr.split()) print('Using Config:') pprint.pprint(args) return args