122 lines
3.4 KiB
Python
122 lines
3.4 KiB
Python
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import numpy as np
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class SimpleTransformer:
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"""
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SimpleTransformer is a simple class for preprocessing and deprocessing
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images for caffe.
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"""
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def __init__(self, mean=[128, 128, 128]):
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self.mean = np.array(mean, dtype=np.float32)
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self.scale = 1.0
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def set_mean(self, mean):
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"""
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Set the mean to subtract for centering the data.
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"""
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self.mean = mean
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def set_scale(self, scale):
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"""
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Set the data scaling.
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"""
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self.scale = scale
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def preprocess(self, im):
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"""
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preprocess() emulate the pre-processing occurring in the vgg16 caffe
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prototxt.
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"""
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im = np.float32(im)
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im = im[:, :, ::-1] # change to BGR
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im -= self.mean
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im *= self.scale
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im = im.transpose((2, 0, 1))
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return im
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def deprocess(self, im):
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"""
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inverse of preprocess()
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"""
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im = im.transpose(1, 2, 0)
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im /= self.scale
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im += self.mean
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im = im[:, :, ::-1] # change to RGB
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return np.uint8(im)
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class CaffeSolver:
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"""
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Caffesolver is a class for creating a solver.prototxt file. It sets default
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values and can export a solver parameter file.
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Note that all parameters are stored as strings. Strings variables are
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stored as strings in strings.
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"""
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def __init__(self, testnet_prototxt_path="testnet.prototxt",
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trainnet_prototxt_path="trainnet.prototxt", debug=False):
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self.sp = {}
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# critical:
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self.sp['base_lr'] = '0.001'
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self.sp['momentum'] = '0.9'
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# speed:
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self.sp['test_iter'] = '100'
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self.sp['test_interval'] = '250'
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# looks:
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self.sp['display'] = '25'
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self.sp['snapshot'] = '2500'
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self.sp['snapshot_prefix'] = '"snapshot"' # string within a string!
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# learning rate policy
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self.sp['lr_policy'] = '"fixed"'
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# important, but rare:
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self.sp['gamma'] = '0.1'
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self.sp['weight_decay'] = '0.0005'
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self.sp['train_net'] = '"' + trainnet_prototxt_path + '"'
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self.sp['test_net'] = '"' + testnet_prototxt_path + '"'
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# pretty much never change these.
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self.sp['max_iter'] = '100000'
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self.sp['test_initialization'] = 'false'
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self.sp['average_loss'] = '25' # this has to do with the display.
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self.sp['iter_size'] = '1' # this is for accumulating gradients
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if (debug):
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self.sp['max_iter'] = '12'
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self.sp['test_iter'] = '1'
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self.sp['test_interval'] = '4'
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self.sp['display'] = '1'
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def add_from_file(self, filepath):
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"""
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Reads a caffe solver prototxt file and updates the Caffesolver
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instance parameters.
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"""
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with open(filepath, 'r') as f:
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for line in f:
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if line[0] == '#':
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continue
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splitLine = line.split(':')
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self.sp[splitLine[0].strip()] = splitLine[1].strip()
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def write(self, filepath):
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"""
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Export solver parameters to INPUT "filepath". Sorted alphabetically.
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"""
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f = open(filepath, 'w')
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for key, value in sorted(self.sp.items()):
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if not(type(value) is str):
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raise TypeError('All solver parameters must be strings')
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f.write('%s: %s\n' % (key, value))
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