Coding style changes
This commit is contained in:
Heiko J Schick
@@ -3,41 +3,33 @@ YOLO v3 object detection with Keras
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Source: https://towardsdatascience.com/yolo-v3-object-detection-with-keras-461d2cfccef6
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"""
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# import os
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# import scipy.io
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# import scipy.misc
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import numpy as np
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# import pandas as pd
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# import PIL
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import struct
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# import cv2
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import glob
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import numpy as np
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from numpy import expand_dims
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# import tensorflow as tf
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# from skimage.transform import resize
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from keras import backend as K
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from keras.layers import Input, Lambda, Conv2D, BatchNormalization, LeakyReLU, ZeroPadding2D, UpSampling2D
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from keras.models import load_model, Model
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from keras.layers import Input, Conv2D, BatchNormalization, LeakyReLU, ZeroPadding2D, UpSampling2D
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from keras.models import Model
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from keras.layers.merge import add, concatenate
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from keras.preprocessing.image import load_img
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from keras.preprocessing.image import img_to_array
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from matplotlib import pyplot
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import matplotlib.pyplot as plt
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from matplotlib.pyplot import imshow
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from matplotlib.patches import Rectangle
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# %matplotlib inline
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"""**Step 1:** `WeightReader` class is used to parse the "yolov3.weights" file and load the model weights into memory in a format that we can set into keras model"""
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# Step 1:
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# WeightReader class is used to parse the "yolov3.weights" file and load the model weights into
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# memory in a format that we can set into keras model
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class WeightReader:
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def __init__(self, weight_file):
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with open(weight_file, 'rb') as w_f:
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major, = struct.unpack('i', w_f.read(4))
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minor, = struct.unpack('i', w_f.read(4))
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revision, = struct.unpack('i', w_f.read(4))
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if (major*10 + minor) >= 2 and major < 1000 and minor < 1000:
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w_f.read(8)
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else:
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w_f.read(4)
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transpose = (major > 1000) or (minor > 1000)
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binary = w_f.read()
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self.offset = 0
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@@ -52,6 +44,7 @@ class WeightReader:
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try:
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conv_layer = model.get_layer('conv_' + str(i))
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print("loading weights of convolution #" + str(i))
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if i not in [81, 93, 105]:
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norm_layer = model.get_layer('bnorm_' + str(i))
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size = np.prod(norm_layer.get_weights()[0].shape)
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@@ -60,6 +53,7 @@ class WeightReader:
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mean = self.read_bytes(size) # mean
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var = self.read_bytes(size) # variance
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weights = norm_layer.set_weights([gamma, beta, mean, var])
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if len(conv_layer.get_weights()) > 1:
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bias = self.read_bytes(np.prod(conv_layer.get_weights()[1].shape))
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kernel = self.read_bytes(np.prod(conv_layer.get_weights()[0].shape))
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@@ -71,110 +65,135 @@ class WeightReader:
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kernel = kernel.reshape(list(reversed(conv_layer.get_weights()[0].shape)))
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kernel = kernel.transpose([2,3,1,0])
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conv_layer.set_weights([kernel])
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except ValueError:
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print("no convolution #" + str(i))
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def reset(self):
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self.offset = 0
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"""**Step 2:**
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- `_conv_block(input, convs, skip=True)` is a function to create convolutional layer
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- `make_yolov3_model()` is a function to create layers of convoluational and stack together as a whole yolo model
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"""
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def _conv_block(inp, convs, skip=True):
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# Step 2:
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# _cb(input, convs, skip=True) is a function to create convolutional layer
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def _cb(inp, convs, skip=True):
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x = inp
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count = 0
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for conv in convs:
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if count == (len(convs) - 2) and skip:
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skip_connection = x
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count += 1
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if conv['stride'] > 1: x = ZeroPadding2D(((1,0),(1,0)))(x) # peculiar padding as darknet prefer left and top
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if conv['stride'] > 1: x = ZeroPadding2D(((1,0),(1,0)))(x) # peculiar padding as darknet
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# prefer left and top
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x = Conv2D(conv['filter'],
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conv['kernel'],
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strides=conv['stride'],
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padding='valid' if conv['stride'] > 1 else 'same', # peculiar padding as darknet prefer left and top
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padding='valid' if conv['stride'] > 1 else 'same', # peculiar padding as darknet
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# prefer left and top
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name='conv_' + str(conv['layer_idx']),
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use_bias=False if conv['bnorm'] else True)(x)
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if conv['bnorm']: x = BatchNormalization(epsilon=0.001, name='bnorm_' + str(conv['layer_idx']))(x)
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if conv['leaky']: x = LeakyReLU(alpha=0.1, name='leaky_' + str(conv['layer_idx']))(x)
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if conv['bnorm']: x = BatchNormalization(epsilon=0.001, name='bnorm_'
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+ str(conv['layer_idx']))(x)
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if conv['leaky']: x = LeakyReLU(alpha=0.1, name='leaky_'
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+ str(conv['layer_idx']))(x)
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return add([skip_connection, x]) if skip else x
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# make_yolov3_model() is a function to create layers of convoluational and stack together as a
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# whole yolo model
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def make_yolov3_model():
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input_image = Input(shape=(None, None, 3))
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# Layer 0 => 4
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x = _conv_block(input_image, [{'filter': 32, 'kernel': 3, 'stride': 1, 'bnorm': True, 'leaky': True, 'layer_idx': 0},
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x = _cb(input_image, [{'filter': 32, 'kernel': 3, 'stride': 1, 'bnorm': True, 'leaky': True, 'layer_idx': 0},
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{'filter': 64, 'kernel': 3, 'stride': 2, 'bnorm': True, 'leaky': True, 'layer_idx': 1},
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{'filter': 32, 'kernel': 1, 'stride': 1, 'bnorm': True, 'leaky': True, 'layer_idx': 2},
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{'filter': 64, 'kernel': 3, 'stride': 1, 'bnorm': True, 'leaky': True, 'layer_idx': 3}])
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# Layer 5 => 8
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x = _conv_block(x, [{'filter': 128, 'kernel': 3, 'stride': 2, 'bnorm': True, 'leaky': True, 'layer_idx': 5},
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x = _cb(x, [{'filter': 128, 'kernel': 3, 'stride': 2, 'bnorm': True, 'leaky': True, 'layer_idx': 5},
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{'filter': 64, 'kernel': 1, 'stride': 1, 'bnorm': True, 'leaky': True, 'layer_idx': 6},
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{'filter': 128, 'kernel': 3, 'stride': 1, 'bnorm': True, 'leaky': True, 'layer_idx': 7}])
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# Layer 9 => 11
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x = _conv_block(x, [{'filter': 64, 'kernel': 1, 'stride': 1, 'bnorm': True, 'leaky': True, 'layer_idx': 9},
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x = _cb(x, [{'filter': 64, 'kernel': 1, 'stride': 1, 'bnorm': True, 'leaky': True, 'layer_idx': 9},
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{'filter': 128, 'kernel': 3, 'stride': 1, 'bnorm': True, 'leaky': True, 'layer_idx': 10}])
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# Layer 12 => 15
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x = _conv_block(x, [{'filter': 256, 'kernel': 3, 'stride': 2, 'bnorm': True, 'leaky': True, 'layer_idx': 12},
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x = _cb(x, [{'filter': 256, 'kernel': 3, 'stride': 2, 'bnorm': True, 'leaky': True, 'layer_idx': 12},
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{'filter': 128, 'kernel': 1, 'stride': 1, 'bnorm': True, 'leaky': True, 'layer_idx': 13},
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{'filter': 256, 'kernel': 3, 'stride': 1, 'bnorm': True, 'leaky': True, 'layer_idx': 14}])
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# Layer 16 => 36
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for i in range(7):
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x = _conv_block(x, [{'filter': 128, 'kernel': 1, 'stride': 1, 'bnorm': True, 'leaky': True, 'layer_idx': 16+i*3},
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x = _cb(x, [{'filter': 128, 'kernel': 1, 'stride': 1, 'bnorm': True, 'leaky': True, 'layer_idx': 16+i*3},
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{'filter': 256, 'kernel': 3, 'stride': 1, 'bnorm': True, 'leaky': True, 'layer_idx': 17+i*3}])
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skip_36 = x
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# Layer 37 => 40
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x = _conv_block(x, [{'filter': 512, 'kernel': 3, 'stride': 2, 'bnorm': True, 'leaky': True, 'layer_idx': 37},
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x = _cb(x, [{'filter': 512, 'kernel': 3, 'stride': 2, 'bnorm': True, 'leaky': True, 'layer_idx': 37},
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{'filter': 256, 'kernel': 1, 'stride': 1, 'bnorm': True, 'leaky': True, 'layer_idx': 38},
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{'filter': 512, 'kernel': 3, 'stride': 1, 'bnorm': True, 'leaky': True, 'layer_idx': 39}])
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# Layer 41 => 61
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for i in range(7):
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x = _conv_block(x, [{'filter': 256, 'kernel': 1, 'stride': 1, 'bnorm': True, 'leaky': True, 'layer_idx': 41+i*3},
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x = _cb(x, [{'filter': 256, 'kernel': 1, 'stride': 1, 'bnorm': True, 'leaky': True, 'layer_idx': 41+i*3},
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{'filter': 512, 'kernel': 3, 'stride': 1, 'bnorm': True, 'leaky': True, 'layer_idx': 42+i*3}])
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skip_61 = x
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# Layer 62 => 65
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x = _conv_block(x, [{'filter': 1024, 'kernel': 3, 'stride': 2, 'bnorm': True, 'leaky': True, 'layer_idx': 62},
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x = _cb(x, [{'filter': 1024, 'kernel': 3, 'stride': 2, 'bnorm': True, 'leaky': True, 'layer_idx': 62},
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{'filter': 512, 'kernel': 1, 'stride': 1, 'bnorm': True, 'leaky': True, 'layer_idx': 63},
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{'filter': 1024, 'kernel': 3, 'stride': 1, 'bnorm': True, 'leaky': True, 'layer_idx': 64}])
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# Layer 66 => 74
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for i in range(3):
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x = _conv_block(x, [{'filter': 512, 'kernel': 1, 'stride': 1, 'bnorm': True, 'leaky': True, 'layer_idx': 66+i*3},
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x = _cb(x, [{'filter': 512, 'kernel': 1, 'stride': 1, 'bnorm': True, 'leaky': True, 'layer_idx': 66+i*3},
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{'filter': 1024, 'kernel': 3, 'stride': 1, 'bnorm': True, 'leaky': True, 'layer_idx': 67+i*3}])
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# Layer 75 => 79
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x = _conv_block(x, [{'filter': 512, 'kernel': 1, 'stride': 1, 'bnorm': True, 'leaky': True, 'layer_idx': 75},
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x = _cb(x, [{'filter': 512, 'kernel': 1, 'stride': 1, 'bnorm': True, 'leaky': True, 'layer_idx': 75},
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{'filter': 1024, 'kernel': 3, 'stride': 1, 'bnorm': True, 'leaky': True, 'layer_idx': 76},
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{'filter': 512, 'kernel': 1, 'stride': 1, 'bnorm': True, 'leaky': True, 'layer_idx': 77},
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{'filter': 1024, 'kernel': 3, 'stride': 1, 'bnorm': True, 'leaky': True, 'layer_idx': 78},
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{'filter': 512, 'kernel': 1, 'stride': 1, 'bnorm': True, 'leaky': True, 'layer_idx': 79}], skip=False)
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{'filter': 512, 'kernel': 1, 'stride': 1, 'bnorm': True, 'leaky': True, 'layer_idx': 79}],
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skip=False)
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# Layer 80 => 82
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yolo_82 = _conv_block(x, [{'filter': 1024, 'kernel': 3, 'stride': 1, 'bnorm': True, 'leaky': True, 'layer_idx': 80},
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{'filter': 255, 'kernel': 1, 'stride': 1, 'bnorm': False, 'leaky': False, 'layer_idx': 81}], skip=False)
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yolo_82 = _cb(x, [{'filter': 1024, 'kernel': 3, 'stride': 1, 'bnorm': True, 'leaky': True, 'layer_idx': 80},
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{'filter': 255, 'kernel': 1, 'stride': 1, 'bnorm': False, 'leaky': False, 'layer_idx': 81}],
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skip=False)
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# Layer 83 => 86
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x = _conv_block(x, [{'filter': 256, 'kernel': 1, 'stride': 1, 'bnorm': True, 'leaky': True, 'layer_idx': 84}], skip=False)
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x = _cb(x, [{'filter': 256, 'kernel': 1, 'stride': 1, 'bnorm': True, 'leaky': True, 'layer_idx': 84}],
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skip=False)
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x = UpSampling2D(2)(x)
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x = concatenate([x, skip_61])
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# Layer 87 => 91
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x = _conv_block(x, [{'filter': 256, 'kernel': 1, 'stride': 1, 'bnorm': True, 'leaky': True, 'layer_idx': 87},
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x = _cb(x, [{'filter': 256, 'kernel': 1, 'stride': 1, 'bnorm': True, 'leaky': True, 'layer_idx': 87},
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{'filter': 512, 'kernel': 3, 'stride': 1, 'bnorm': True, 'leaky': True, 'layer_idx': 88},
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{'filter': 256, 'kernel': 1, 'stride': 1, 'bnorm': True, 'leaky': True, 'layer_idx': 89},
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{'filter': 512, 'kernel': 3, 'stride': 1, 'bnorm': True, 'leaky': True, 'layer_idx': 90},
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{'filter': 256, 'kernel': 1, 'stride': 1, 'bnorm': True, 'leaky': True, 'layer_idx': 91}], skip=False)
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# Layer 92 => 94
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yolo_94 = _conv_block(x, [{'filter': 512, 'kernel': 3, 'stride': 1, 'bnorm': True, 'leaky': True, 'layer_idx': 92},
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yolo_94 = _cb(x, [{'filter': 512, 'kernel': 3, 'stride': 1, 'bnorm': True, 'leaky': True, 'layer_idx': 92},
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{'filter': 255, 'kernel': 1, 'stride': 1, 'bnorm': False, 'leaky': False, 'layer_idx': 93}], skip=False)
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# Layer 95 => 98
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x = _conv_block(x, [{'filter': 128, 'kernel': 1, 'stride': 1, 'bnorm': True, 'leaky': True, 'layer_idx': 96}], skip=False)
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x = _cb(x, [{'filter': 128, 'kernel': 1, 'stride': 1, 'bnorm': True, 'leaky': True, 'layer_idx': 96}], skip=False)
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x = UpSampling2D(2)(x)
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x = concatenate([x, skip_36])
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# Layer 99 => 106
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yolo_106 = _conv_block(x, [{'filter': 128, 'kernel': 1, 'stride': 1, 'bnorm': True, 'leaky': True, 'layer_idx': 99},
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yolo_106 = _cb(x, [{'filter': 128, 'kernel': 1, 'stride': 1, 'bnorm': True, 'leaky': True, 'layer_idx': 99},
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{'filter': 256, 'kernel': 3, 'stride': 1, 'bnorm': True, 'leaky': True, 'layer_idx': 100},
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{'filter': 128, 'kernel': 1, 'stride': 1, 'bnorm': True, 'leaky': True, 'layer_idx': 101},
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{'filter': 256, 'kernel': 3, 'stride': 1, 'bnorm': True, 'leaky': True, 'layer_idx': 102},
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{'filter': 128, 'kernel': 1, 'stride': 1, 'bnorm': True, 'leaky': True, 'layer_idx': 103},
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{'filter': 256, 'kernel': 3, 'stride': 1, 'bnorm': True, 'leaky': True, 'layer_idx': 104},
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{'filter': 255, 'kernel': 1, 'stride': 1, 'bnorm': False, 'leaky': False, 'layer_idx': 105}], skip=False)
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model = Model(input_image, [yolo_82, yolo_94, yolo_106])
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return model
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"""**Step 3:**
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@@ -394,8 +413,6 @@ labels = ["person", "bicycle", "car", "motorbike", "aeroplane", "bus", "train",
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# from google.colab import files
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# upload = files.upload()
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import glob
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for photo_filename in glob.glob("images/test/dog/*"):
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# for fn in upload.keys():
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@@ -434,4 +451,3 @@ for photo_filename in glob.glob("images/test/dog/*"):
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draw_boxes(photo_filename, v_boxes, v_labels, v_scores)
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print([a.shape for a in yhat])
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