updates
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@ -11,7 +11,7 @@ sudo reboot now
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# Re-clone
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sudo rm -rf yolov3
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# git clone https://github.com/ultralytics/yolov3 # master
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git clone -b map_update --depth 1 https://github.com/ultralytics/yolov3 yolov3 # branch
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git clone -b test --depth 1 https://github.com/ultralytics/yolov3 yolov3_test # branch
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cp -r weights yolov3
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cp -r cocoapi/PythonAPI/pycocotools yolov3
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cd yolov3
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@ -3,6 +3,7 @@ import random
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from collections import defaultdict
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import cv2
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import matplotlib
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import matplotlib.pyplot as plt
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import numpy as np
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import torch
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@ -10,6 +11,8 @@ import torch.nn as nn
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from utils import torch_utils
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matplotlib.rc('font', **{'family': 'normal', 'size': 11})
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# Set printoptions
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torch.set_printoptions(linewidth=1320, precision=5, profile='long')
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np.set_printoptions(linewidth=320, formatter={'float_kind': '{:11.5g}'.format}) # format short g, %precision=5
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@ -286,8 +289,8 @@ def compute_loss(p, targets): # predictions, targets
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# Add to dictionary
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d = defaultdict(float)
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losses = [loss.item(), lxy.item(), lwh.item(), lconf.item(), lcls.item()]
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for name, x in zip(['total', 'xy', 'wh', 'conf', 'cls'], losses):
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d[name] = x
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for k, v in zip(['total', 'xy', 'wh', 'conf', 'cls'], losses):
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d[k] = v
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return loss, d
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@ -325,7 +328,7 @@ def build_targets(model, targets):
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# Width and height
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twh.append(torch.log(gwh / layer.anchor_vec[a])) # yolo method
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# twh.append(torch.sqrt(gwh / layer.anchor_vec[a]) / 2) # power method
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# twh.append((gwh / layer.anchor_vec[a]) ** (1 / 3) / 2) # power method
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# Class
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tcls.append(c)
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@ -468,7 +471,7 @@ def plot_wh_methods(): # from utils.utils import *; plot_wh_methods()
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# https://github.com/ultralytics/yolov3/issues/168
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x = np.arange(-4.0, 4.0, .1)
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ya = np.exp(x)
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yb = (torch.sigmoid(torch.from_numpy(x)).numpy() * 2)
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yb = torch.sigmoid(torch.from_numpy(x)).numpy() * 2
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fig = plt.figure(figsize=(6, 3), dpi=150)
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plt.plot(x, ya, '.-', label='yolo method')
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@ -495,7 +498,7 @@ def plot_results(start=0, stop=0): # from utils.utils import *; plot_results()
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x = range(start, stop if stop else results.shape[1])
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for i in range(10):
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plt.subplot(2, 5, i + 1)
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plt.plot(x, results[i, x], marker='.', label=f)
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plt.plot(x, results[i, x].clip(max=500), marker='.', label=f)
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plt.title(s[i])
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if i == 0:
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plt.legend()
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