# Copyright (c) 2018 - 2022 Advanced Micro Devices, Inc. All rights reserved. # # Permission is hereby granted, free of charge, to any person obtaining a copy # of this software and associated documentation files (the "Software"), to deal # in the Software without restriction, including without limitation the rights # to use, copy, modify, merge, publish, distribute, sublicense, and/or sell # copies of the Software, and to permit persons to whom the Software is # furnished to do so, subject to the following conditions: # # The above copyright notice and this permission notice shall be included in # all copies or substantial portions of the Software. # # THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR # IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, # FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE # AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER # LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, # OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN # THE SOFTWARE. #!/usr/bin/python from rali import * from rali_image_iterator import * from rali_common import * import cv2 import time import sys # Defining pipeline """ class DataLoader(RaliGraph): def __init__(self, input_path, batch_size, input_color_format, affinity): RaliGraph.__init__(self, batch_size, affinity) self.setSeed(0) self.jpg_img = self.jpegFileInput(input_path, input_color_format, False, 0) self.input = self.cropResize(self.jpg_img, 112,112, False, 0.6, -1, -1) #self.input = self.resize(self.jpg_img, 112,112,False) # self.warped = self.warpAffine(self.input,True) self.contrast_img = self.colorTwist(self.input,True) self.rain_img = self.rain(self.contrast_img, True) self.bright_img = self.brightness(self.input,False) self.temp_img = self.colorTemp(self.bright_img, True) self.exposed_img = self.exposure(self.input, False) self.vignette_img = self.vignette(self.exposed_img, False) self.fog_img = self.fog(self.vignette_img, False) self.snow_img = self.snow(self.fog_img, True) self.pixelate_img = self.pixelate(self.input, False) self.snp_img = self.SnPNoise(self.pixelate_img, False) self.gamma_img = self.gamma(self.snp_img, True) self.rotate_img = self.rotate(self.input, False) self.jitter_img = self.jitter(self.rotate_img, True) def get_input_name(self, idx): return self.jpg_img.name(idx) #%% """ class DataLoader(RaliGraph): def __init__(self, input_path, rali_batch_size, model_batch_size, input_color_format, affinity, image_validation, h_img, w_img, raliMode, loop_parameter, tensor_layout = TensorLayout.NCHW, reverse_channels = False, multiplier = [1.0,1.0,1.0], offset = [0.0, 0.0, 0.0], tensor_dtype=TensorDataType.FLOAT32): RaliGraph.__init__(self, rali_batch_size, affinity) self.validation_dict = {} self.process_validation(image_validation) self.setSeed(0) self.aug_strength = 0 #params for contrast self.min_param = RaliIntParameter(0) self.max_param = RaliIntParameter(255) #param for brightness self.alpha_param = RaliFloatParameter(0.0) #param for colorTemp self.adjustment_param = RaliIntParameter(0) #param for exposure self.shift_param = RaliFloatParameter(0.0) #param for SnPNoise self.sdev_param = RaliFloatParameter(0.0) #param for gamma self.gamma_shift_param = RaliFloatParameter(0.0) #param for rotate self.degree_param = RaliFloatParameter(0.0) #rocAL list of augmentation self.rali_list = None if model_batch_size == 16: if raliMode == 1: self.jpg_img = self.jpegFileInput(input_path, input_color_format, False, loop_parameter, 0) self.input = self.resize(self.jpg_img, h_img, w_img, True) self.warped = self.warpAffine(self.input,True) self.contrast_img = self.contrast(self.input,True, self.min_param, self.max_param) self.rain_img = self.rain(self.input, True) self.bright_img = self.brightness(self.input,True, self.alpha_param) self.temp_img = self.colorTemp(self.input, True, self.adjustment_param) self.exposed_img = self.exposure(self.input, True, self.shift_param) self.vignette_img = self.vignette(self.input, True) self.blur_img = self.blur(self.input, True) self.snow_img = self.snow(self.input, True) self.pixelate_img = self.pixelate(self.input, True) self.snp_img = self.SnPNoise(self.input, True, self.sdev_param) self.gamma_img = self.gamma(self.input, True, self.gamma_shift_param) self.rotate_img = self.rotate(self.input, True, self.degree_param) self.flip_img = self.flip(self.input, True, 1) #self.jitter_img = self.jitter(self.input, True) self.blend_img = self.blend(self.input, self.contrast_img, True) elif raliMode == 2: self.jpg_img = self.jpegFileInput(input_path, input_color_format, False, loop_parameter, 0) self.input = self.resize(self.jpg_img, h_img, w_img, True) self.warped = self.warpAffine(self.input,True) self.contrast_img = self.contrast(self.input,True, self.min_param, self.max_param) self.rain_img = self.rain(self.contrast_img, True) self.bright_img = self.brightness(self.input,True, self.alpha_param) self.temp_img = self.colorTemp(self.bright_img, True, self.adjustment_param) self.exposed_img = self.exposure(self.input, True, self.shift_param) self.vignette_itensor_dtypemg = self.vignette(self.exposed_img, True) self.blur_img = self.blur(self.input, True) self.snow_img = self.snow(self.blur_img, True) self.pixelate_img = self.pixelate(self.input, True) self.snp_img = self.SnPNoise(self.pixelate_img, True, self.sdev_param) self.gamma_img = self.gamma(self.input, True, self.gamma_shift_param) self.rotate_img = self.rotate(self.input, True, self.degree_param) self.flip_img = self.flip(self.input, True, 1) #self.jitter_img = self.jitter(self.rotate_img, True) self.blend_img = self.blend(self.rotate_img, self.warped, True) elif raliMode == 3: self.jpg_img = self.jpegFileInput(input_path, input_color_format, False, loop_parameter, 0) self.input = self.resize(self.jpg_img, h_img, w_img, True) self.warped = self.warpAffine(self.input,True) self.contrast_img = self.contrast(self.input,True, self.min_param, self.max_param) self.rain_img = self.rain(self.warped, True) self.bright_img = self.brightness(self.input,True, self.alpha_param) self.temp_img = self.colorTemp(self.input, True, self.adjustment_param) self.exposed_img = self.exposure(self.input, True, self.shift_param) self.vignette_img = self.vignette(self.input, True) self.blur_img = self.blur(self.input, True) self.snow_img = self.snow(self.vignette_img, True) self.pixelate_img = self.pixelate(self.input, True) self.gamma_img = self.gamma(self.input, True, self.gamma_shift_param) self.snp_img = self.SnPNoise(self.gamma_img, True, self.sdev_param) self.rotate_img = self.rotate(self.input, True, self.degree_param) self.flip_img = self.flip(self.input, True, 1) #self.jitter_img = self.jitter(self.pixelate_img, True) self.blend_img = self.blend(self.snow_img, self.bright_img, True) elif raliMode == 4: self.jpg_img = self.jpegFileInput(input_path, input_color_format, False, loop_parameter, 0) self.input = self.resize(self.jpg_img, h_img, w_img, True) for i in range(15): self.copy_img = self.copy(self.input, True) elif raliMode == 5: self.jpg_img = self.jpegFileInput(input_path, input_color_format, False, loop_parameter, 0) self.input = self.resize(self.jpg_img, h_img, w_img, True) for i in range(15): self.nop_img = self.nop(self.input, True) elif model_batch_size == 64: if raliMode == 1: self.jpg_img = self.jpegFileInput(input_path, input_color_format, False, loop_parameter, 0) self.input = self.resize(self.jpg_img, h_img, w_img, False) self.rot150_img = self.rotate(self.input, False, 150) self.flip_img = self.flip(self.input, False) self.rot45_img = self.rotate(self.input, False, 45) self.setof16_mode1(self.input, h_img, w_img) self.setof16_mode1(self.rot45_img, h_img, w_img) self.setof16_mode1(self.flip_img, h_img, w_img) self.setof16_mode1(self.rot150_img , h_img, w_img) elif raliMode == 2: self.jpg_img = self.jpegFileInput(input_path, input_color_format, False, loop_parameter, 0) self.input = self.resize(self.jpg_img, h_img, w_img, False) #self.warpAffine2_img = self.warpAffine(self.input, False, [[1.5,0],[0,1],[None,None]]) self.warpAffine1_img = self.warpAffine(self.input, False, [[0.5,0],[0,2],[None,None]]) #squeeze self.fishEye_img = self.fishEye(self.input, False) self.lensCorrection_img = self.lensCorrection(self.input, False, 1.5, 2) self.setof16_mode1(self.input, h_img, w_img) self.setof16_mode1(self.warpAffine1_img, h_img, w_img) self.setof16_mode1(self.fishEye_img, h_img, w_img) self.setof16_mode1(self.lensCorrection_img, h_img, w_img) elif raliMode == 3: self.jpg_img = self.jpegFileInput(input_path, input_color_format, False, loop_parameter, 0) self.input = self.resize(self.jpg_img, h_img, w_img, False) self.colorTemp1_img = self.colorTemp(self.input, False, 10) self.colorTemp2_img = self.colorTemp(self.input, False, 20) self.warpAffine2_img = self.warpAffine(self.input, False, [[2,0],[0,1],[None,None]]) #stretch self.setof16_mode1(self.input, h_img, w_img) self.setof16_mode1(self.colorTemp1_img, h_img, w_img) self.setof16_mode1(self.colorTemp2_img, h_img, w_img) self.setof16_mode1(self.warpAffine2_img , h_img, w_img) elif raliMode == 4: self.jpg_img = self.jpegFileInput(input_path, input_color_format, False, loop_parameter, 0) self.input = self.resize(self.jpg_img, h_img, w_img, True) for i in range(63): self.copy_img = self.copy(self.input, True) elif raliMode == 5: self.jpg_img = self.jpegFileInput(input_path, input_color_format, False, loop_parameter, 0) self.input = self.resize(self.jpg_img, h_img, w_img, True) for i in range(63): self.nop_img = self.nop(self.input, True) #rocAL iterator if self.build() != 0: raise Exception('Failed to build the augmentation graph') self.tensor_format =tensor_layout self.multiplier = multiplier self.offset = offset self.reverse_channels = reverse_channels self.tensor_dtype = tensor_dtype self.w = self.getOutputWidth() self.h = self.getOutputHeight() self.b = self.getBatchSize() self.n = self.raliGetAugmentationBranchCount() color_format = self.getOutputColorFormat() self.p = (1 if color_format is ColorFormat.IMAGE_U8 else 3) height = self.h*self.n self.out_image = np.zeros((height, self.w, self.p), dtype = "uint8") self.out_tensor = np.zeros(( self.b*self.n, self.p, self.h/self.b, self.w,), dtype = "float32") def get_input_name(self): size = self.raliGetImageNameLen(0) ret = ctypes.create_string_buffer(size) self.raliGetImageName(ret, 0) return ret.value def process_validation(self, validation_list): for i in range(len(validation_list)): name, groundTruthIndex = validation_list[i].decode("utf-8").split(' ') self.validation_dict[name] = groundTruthIndex def get_ground_truth(self): return self.validation_dict[self.get_input_name()] def setof16_mode1(self, input_image, h_img, w_img): self.resized_image = self.resize(input_image, h_img, w_img, True) self.warped = self.warpAffine(input_image,True) self.contrast_img = self.contrast(input_image,True, self.min_param, self.max_param) self.rain_img = self.rain(input_image, True) self.bright_img = self.brightness(input_image,True, self.alpha_param) self.temp_img = self.colorTemp(input_image, True, self.adjustment_param) self.exposed_img = self.exposure(input_image, True, self.shift_param) self.vignette_img = self.vignette(input_image, True) self.blur_img = self.blur(input_image, True) self.snow_img = self.snow(input_image, True) self.pixelate_img = self.pixelate(input_image, True) self.snp_img = self.SnPNoise(input_image, True, self.sdev_param) self.gamma_img = self.gamma(input_image, True, self.gamma_shift_param) self.rotate_img = self.rotate(input_image, True, self.degree_param) self.flip_img = self.flip(input_image, True, 1) #self.jitter_img = self.jitter(input_image, True) self.blend_img = self.blend(input_image, self.contrast_img, True) def updateAugmentationParameter(self, augmentation): #values for contrast self.aug_strength = augmentation min = int(augmentation*100) max = 150 + int((1-augmentation)*100) self.min_param.update(min) self.max_param.update(max) #values for brightness alpha = augmentation*1.95 self.alpha_param.update(alpha) #values for colorTemp adjustment = (augmentation*99) if ((int(augmentation*100)) % 2 == 0) else (-1*augmentation*99) adjustment = int(adjustment) self.adjustment_param.update(adjustment) #values for exposure shift = augmentation*0.95 self.shift_param.update(shift) #values for SnPNoise sdev = augmentation*0.7 self.sdev_param.update(sdev) #values for gamma gamma_shift = augmentation*5.0 self.gamma_shift_param.update(gamma_shift) def renew_parameters(self): curr_degree = self.degree_param.get() #values for rotation change degree = self.aug_strength * 100 self.degree_param.update(curr_degree+degree) def start_iterator(self): self.reset() def get_next_augmentation(self): if self.raliIsEmpty() == 1: #raise StopIteration return -1 self.renew_parameters() if self.run() != 0: #raise StopIteration return -1 self.copyToNPArray(self.out_image) if(TensorLayout.NCHW == self.tensor_format): self.copyToTensorNCHW(self.out_tensor, self.multiplier, self.offset, self.reverse_channels, self.tensor_dtype) else: self.copyToTensorNHWC(self.out_tensor, self.multiplier, self.offset, self.reverse_channels, self.tensor_dtype) return self.out_image , self.out_tensor def get_rali_list(self, raliMode, model_batch_size): if model_batch_size == 16: if raliMode == 1: self.rali_list = raliList_mode1_16 elif raliMode == 2: self.rali_list = raliList_mode2_16 elif raliMode == 3: self.rali_list = raliList_mode3_16 elif raliMode == 4: self.rali_list = raliList_mode4_16 elif raliMode == 5: self.rali_list = raliList_mode5_16 elif model_batch_size == 64: if raliMode == 1: self.rali_list = raliList_mode1_64 elif raliMode == 2: self.rali_list = raliList_mode2_64 elif raliMode == 3: self.rali_list = raliList_mode3_64 elif raliMode == 4: self.rali_list = raliList_mode4_64 elif raliMode == 5: self.rali_list = raliList_mode5_64 return self.rali_list def main(): batchSize = 2 if len(sys.argv) < 3: print ('Please pass the folder containing images as a command line argument plus path to the labels file') exit(0) input_path = sys.argv[1] label_file_path = sys.argv[2] # Pipeline creation #loader = DataLoader(input_path, batchSize, ColorFormat.IMAGE_RGB24, Affinity.PROCESS_GPU) rali_batch_size = 1 modelBatchSizeInt = 64 fp = open(label_file_path, 'r') imageValidation = fp.readlines() fp.close() rali_mode = 1 loop = False Mx = [1,1,1] Ax = [0,0,0] loader = DataLoader(input_path, rali_batch_size, modelBatchSizeInt, ColorFormat.IMAGE_RGB24, Affinity.PROCESS_CPU, imageValidation, 224, 224, rali_mode, loop, TensorLayout.NCHW, False, Mx, Ax, TensorDataType.FLOAT32) imageIterator = ImageIterator(loader, tensor_dtype=TensorDataType.FLOAT32) print ("Input shape", loader.input.shape()) print ('Pipeline created ...') print ('Image iterator created ... number of images', imageIterator.imageCount()) start = time.time() for i, (image_batch, image_tensor) in enumerate(imageIterator, 0): cv2.imshow('image', cv2.cvtColor(image_batch, cv2.COLOR_RGB2BGR)) print (loader.get_input_name(0)) cv2.waitKey(0) end = time.time() print ('Elapsed time1 ',(end - start)) if __name__ == '__main__': main() #%%