全球新型冠状病毒肺炎疫情分布

diff --git a/.DS_Store b/.DS_Store deleted file mode 100644 index 85bb286..0000000 Binary files a/.DS_Store and /dev/null differ diff --git a/chaoxi/2020-11-08-plotly/plotpy_test.ipynb b/chaoxi/2020-11-08-plotly/plotpy_test.ipynb new file mode 100644 index 0000000..5c29639 --- /dev/null +++ b/chaoxi/2020-11-08-plotly/plotpy_test.ipynb @@ -0,0 +1,4975 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "# import pkg\n", + "from plotly.graph_objs import Scatter,Layout\n", + "import plotly\n", + "import plotly.offline as py\n", + "import numpy as np\n", + "import plotly.graph_objs as go" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + " \n", + " " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "#setting offilne\n", + "plotly.offline.init_notebook_mode(connected=True)" + ] + }, + { + "cell_type": "code", + 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0, + "ticks": "" + }, + "colorscale": [ + [ + 0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1, + "#f0f921" + ] + ], + "type": "surface" + } + ], + "table": [ + { + "cells": { + "fill": { + "color": "#EBF0F8" + }, + "line": { + "color": "white" + } + }, + "header": { + "fill": { + "color": "#C8D4E3" + }, + "line": { + "color": "white" + } + }, + "type": "table" + } + ] + }, + "layout": { + "annotationdefaults": { + "arrowcolor": "#2a3f5f", + "arrowhead": 0, + "arrowwidth": 1 + }, + "coloraxis": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "colorscale": { + "diverging": [ + [ + 0, + "#8e0152" + ], + [ + 0.1, + "#c51b7d" + ], + [ + 0.2, + "#de77ae" + ], + [ + 0.3, + "#f1b6da" + ], + [ + 0.4, + "#fde0ef" + ], + [ + 0.5, + "#f7f7f7" + ], + [ + 0.6, + "#e6f5d0" + ], + [ + 0.7, + "#b8e186" + ], + [ + 0.8, + "#7fbc41" + ], + [ + 0.9, + "#4d9221" + ], + [ + 1, + "#276419" + ] + ], + "sequential": [ + [ + 0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1, + "#f0f921" + ] + ], + "sequentialminus": [ + [ + 0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1, + "#f0f921" + ] + ] + }, + "colorway": [ + "#636efa", + "#EF553B", + "#00cc96", + "#ab63fa", + "#FFA15A", + "#19d3f3", + "#FF6692", + "#B6E880", + "#FF97FF", + "#FECB52" + ], + "font": { + "color": "#2a3f5f" + }, + "geo": { + "bgcolor": "white", + "lakecolor": "white", + "landcolor": "#E5ECF6", + "showlakes": true, + "showland": true, + "subunitcolor": "white" + }, + "hoverlabel": { + "align": "left" + }, + "hovermode": "closest", + "mapbox": { + "style": "light" + }, + "paper_bgcolor": "white", + "plot_bgcolor": "#E5ECF6", + "polar": { + "angularaxis": { + "gridcolor": "white", + "linecolor": "white", + "ticks": "" + }, + "bgcolor": "#E5ECF6", + "radialaxis": { + "gridcolor": "white", + "linecolor": "white", + "ticks": "" + } + }, + "scene": { + "xaxis": { + "backgroundcolor": "#E5ECF6", + "gridcolor": "white", + "gridwidth": 2, + "linecolor": "white", + "showbackground": true, + "ticks": "", + "zerolinecolor": "white" + }, + "yaxis": { + "backgroundcolor": "#E5ECF6", + "gridcolor": "white", + "gridwidth": 2, + "linecolor": "white", + "showbackground": true, + "ticks": "", + "zerolinecolor": "white" + }, + "zaxis": { + "backgroundcolor": "#E5ECF6", + "gridcolor": "white", + "gridwidth": 2, + "linecolor": "white", + "showbackground": true, + "ticks": "", + "zerolinecolor": "white" + } + }, + "shapedefaults": { + "line": { + "color": "#2a3f5f" + } + }, + "ternary": { + "aaxis": { + "gridcolor": "white", + "linecolor": "white", + "ticks": "" + }, + "baxis": { + "gridcolor": "white", + "linecolor": "white", + "ticks": "" + }, + "bgcolor": "#E5ECF6", + "caxis": { + "gridcolor": "white", + "linecolor": "white", + "ticks": "" + } + }, + "title": { + "x": 0.05 + }, + "xaxis": { + "automargin": true, + "gridcolor": "white", + "linecolor": "white", + "ticks": "", + "title": { + "standoff": 15 + }, + "zerolinecolor": "white", + "zerolinewidth": 2 + }, + "yaxis": { + "automargin": true, + "gridcolor": "white", + "linecolor": "white", + "ticks": "", + "title": { + "standoff": 15 + }, + "zerolinecolor": "white", + "zerolinewidth": 2 + } + } + } + } + }, + "text/html": [ + "

" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# 散点图\n", + "trace1 = go.Scatter(\n", + " y = np.random.randn(700),\n", + " mode = 'markers',\n", + " marker = dict(\n", + " size = 16,\n", + " color = np.random.randn(800),\n", + " colorscale = 'Viridis',\n", + " showscale = True\n", + " )\n", + ")\n", + "data = [trace1]\n", + "py.iplot(data)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.6" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/chaoxi/521_love/love.py b/chaoxi/521_love/love.py new file mode 100755 index 0000000..176cacb --- /dev/null +++ b/chaoxi/521_love/love.py @@ -0,0 +1,63 @@ +import matplotlib.pyplot as plt +import seaborn +import numpy + +# 定义方法 +def draw_love(): + #拼凑字母 + l = numpy.arange(0, 4, 0.01) + L = 1.0 / l + theta = numpy.arange(-4, 4, 0.01) + o = 3.0 * numpy.cos(theta) + O = 3.0 * numpy.sin(theta) + v = numpy.arange(-4, 4, 0.01) + V = numpy.abs(-2.0 * v) + e = numpy.arange(-3, 3, 0.01) + E = -1.0 * numpy.abs(numpy.sin(e)) + y = numpy.arange(-10, 10, 0.01) + Y = numpy.log2(numpy.abs(y)) + u = numpy.arange(-4, 4, 0.01) + U = 2.0 * u ** 2 + points = [] + + for heartY in numpy.linspace(-100, 100, 500): + for heartX in numpy.linspace(-100, 100, 500): + if ((heartX * 0.03) ** 2 + (heartY * 0.03) ** 2 - 1) ** 3 - (heartX * 0.03) ** 2 * ( + heartY * 0.03) ** 3 <= 0: + points.append({"x": heartX, "y": heartY}) +# 设置直角坐标系 + heart_x = list(map(lambda point: point["x"], points)) + heart_y = list(map(lambda point: point["y"], points)) + +# 添加网格 + fig = plt.figure(figsize=(13, 7)) + ax_L = fig.add_subplot(2, 4, 1) + ax_O = fig.add_subplot(2, 4, 2) + ax_V = fig.add_subplot(2, 4, 3) + ax_E = fig.add_subplot(2, 4, 4) + ax_Y = fig.add_subplot(2, 4, 5) + ax_O_2 = fig.add_subplot(2, 4, 6) + ax_U = fig.add_subplot(2, 4, 7) + ax_heart = fig.add_subplot(2, 4, 8) + + # 设置坐标 + ax_L.plot(l, L) + ax_O.plot(o, O) + ax_V.plot(v, V) + ax_E.plot(E, e) + ax_Y.plot(y, Y) + ax_Y.axis([-10.0, 10.0, -10.0, 5.0]) + ax_O_2.plot(o, O) + + ax_U.plot(u, U) + + ax_heart.scatter(heart_x, heart_y, s=10, alpha=0.5) + # 设置颜色 + plt.plot(color='red') + # 展示结果 + plt.show() + +# 主函数 +if __name__ == '__main__': + seaborn.set_style('dark') + draw_love() \ No newline at end of file diff --git a/chaoxi/521_love/love1.py b/chaoxi/521_love/love1.py new file mode 100644 index 0000000..21c8370 --- /dev/null +++ b/chaoxi/521_love/love1.py @@ -0,0 +1,79 @@ +import turtle +import time + +def hart_arc(): + for i in range(200): + turtle.right(1) + turtle.forward(2) + + +def move_pen_position(x, y): + turtle.hideturtle() # 隐藏画笔（先） + turtle.up() # 提笔 + turtle.goto(x, y) # 移动画笔到指定起始坐标（窗口中心为0,0） + turtle.down() # 下笔 + turtle.showturtle() # 显示画笔 + + +love = input("请输入表白话语：") +signature = input("请签署你的名字:") +date=input("请写上日期：") + +if love == '': + love = 'I Love You' + +turtle.setup(width=800, height=500) # 窗口（画布）大小 +turtle.color('red', 'pink') # 画笔颜色 +turtle.pensize(3) # 画笔粗细 +turtle.speed(1) # 描绘速度 +# 初始化画笔起始坐标 +move_pen_position(x=0, y=-180) # 移动画笔位置 +turtle.left(140) # 向左旋转140度 + +turtle.begin_fill() # 标记背景填充位置 + +# 画图和展示 +turtle.forward(224) # 向前移动画笔，长度为224 +# 画爱心圆弧 +hart_arc() # 左侧圆弧 +turtle.left(120) # 调整画笔角度 +hart_arc() # 右侧圆弧 +# 画心形直线（右下方） +turtle.forward(224) + +turtle.end_fill() # 标记背景填充结束位置 + +move_pen_position(x=70, y=160) # 移动画笔位置 +turtle.left(185) # 向左旋转180度 +turtle.circle(-110,185) # 右侧圆弧 +# 画心形直线（右下方） +#turtle.left(20) # 向左旋转180度 +turtle.forward(50) +move_pen_position(x=-180, y=-180) # 移动画笔位置 +turtle.left(180) # 向左旋转140度 + +# 画心形直线（左下方） +turtle.forward(600) # 向前移动画笔，长度为224 + +# 在心形中写上表白话语 +move_pen_position(0,50) # 表白语位置 +turtle.hideturtle() # 隐藏画笔 +turtle.color('#CD5C5C', 'pink') # 字体颜色 +# font:设定字体、尺寸（电脑下存在的字体都可设置） align:中心对齐 +turtle.write(love, font=('Arial', 20, 'bold'), align="center") + +# 签写署名和日期 +if (signature != '') & (date != ''): + turtle.color('red', 'pink') + time.sleep(2) + move_pen_position(220, -180) + turtle.hideturtle() # 隐藏画笔 + turtle.write(signature, font=('Arial', 20), align="center") + move_pen_position(220, -220) + turtle.hideturtle() # 隐藏画笔 + turtle.write(date, font=('Arial', 20), align="center") + +#点击窗口关闭程序 +window = turtle.Screen() +window.exitonclick() + diff --git a/chaoxi/BaseMap/Python_BaseMap1.py b/chaoxi/BaseMap/Python_BaseMap1.py new file mode 100644 index 0000000..90d9a47 --- /dev/null +++ b/chaoxi/BaseMap/Python_BaseMap1.py @@ -0,0 +1,96 @@ +import matplotlib.pyplot as plt +from mpl_toolkits.basemap import Basemap +import numpy as np + +# 画海岸线 +def drawcoast(): + + plt.figure(figsize=(12, 8)) + m = Basemap() # 创建一个地图 + m.drawcoastlines() # 画海岸线 + plt.show() # 显示图像 + +# 地球基本画法 +def draw_basic(): + map = Basemap(projection='ortho', lat_0=0, lon_0=0) + map.drawmapboundary(fill_color='aqua') + map.fillcontinents(color='gray',lake_color='aqua') + map.drawcoastlines() + plt.show() + +# 画中国地图 +def draw_china(): + plt.figure(figsize=(10, 6)) + m = Basemap(llcrnrlon=77, llcrnrlat=14, urcrnrlon=140, urcrnrlat=51, projection='lcc', lat_1=33, lat_2=45, + lon_0=100) + m.drawcountries(linewidth=1.5) + m.drawcoastlines() + plt.show() + +# 画地球人口分布图 +def drawearth(): + names = [] + pops = [] + lats = [] + lons = [] + countries = [] + file = open("data/main_city", encoding='utf-8').readlines() + for line in file: + info = line.split() + names.append(info[0]) + pops.append(float(info[1])) + lat = float(info[2][:-1]) + if info[2][-1] == 'S': lat = -lat + lats.append(lat) + lon = float(info[3][:-1]) + if info[3][-1] == 'W': lon = -lon + 360.0 + lons.append(lon) + country = info[4] + countries.append(country) + # set up map projection with + # use low resolution coastlines. + map = Basemap(projection='ortho', lat_0=35, lon_0=120, resolution='l') + # draw coastlines, country boundaries, fill continents. + map.drawcoastlines(linewidth=0.25) + map.drawcountries(linewidth=0.25) + # draw the edge of the map projection region (the projection limb) + map.drawmapboundary(fill_color='#689CD2') + # draw lat/lon grid lines every 30 degrees. + map.drawmeridians(np.arange(0, 360, 30)) + map.drawparallels(np.arange(-90, 90, 30)) + # Fill continent wit a different color + map.fillcontinents(color='#BF9E30', lake_color='#689CD2', zorder=0) + # compute native map projection coordinates of lat/lon grid. + x, y = map(lons, lats) + max_pop = max(pops) + # Plot each city in a loop. + # Set some parameters + size_factor = 80.0 + y_offset = 15.0 + rotation = 30 + for i, j, k, name in zip(x, y, pops, names): + size = size_factor * k / max_pop + cs = map.scatter(i, j, s=size, marker='o', color='#FF5600') + plt.text(i, j + y_offset, name, rotation=rotation, fontsize=10) + + plt.title('earth') + plt.show() + +# 画带投影的地球图片 +def draw_earth1(): + import matplotlib.pyplot as plt + from mpl_toolkits.basemap import Basemap + plt.figure(figsize=(8, 8)) + # 正射投影，投影原点设在了上海周边 + m = Basemap(projection='ortho', resolution=None, lat_0=30, lon_0=120) + # 图像原始分辨率是5400*2700，设置scale = 0.5以后分辨率为2700*1350,如此作图 + # 迅速不少也不那么占用内存了 + m.bluemarble(scale=0.5) + plt.show() + +if __name__ == '__main__': + #drawcoast() + drawearth() + #draw_basic() + #draw_china() + #draw_earth1() \ No newline at end of file diff --git a/chaoxi/BaseMap/main_city b/chaoxi/BaseMap/main_city new file mode 100644 index 0000000..493626a --- /dev/null +++ b/chaoxi/BaseMap/main_city @@ -0,0 +1,9 @@ +Shanghai 23019148 31.23N 121.47E China +Mumbai 12478447 18.96N 72.82E India +Karachi 13050000 24.86N 67.01E Pakistan +Delhi 16314838 28.67N 77.21E India +Manila 11855975 14.62N 120.97E Philippines +Seoul 23616000 37.56N 126.99E Korea(South) +Jakarta 28019545 6.18S 106.83E Indonesia +Tokyo 35682460 35.67N 139.77E Japan +Peking 19612368 39.91N 116.39E China diff --git a/chaoxi/FilesToPDF/files2pdf.py b/chaoxi/FilesToPDF/files2pdf.py new file mode 100644 index 0000000..4096c90 --- /dev/null +++ b/chaoxi/FilesToPDF/files2pdf.py @@ -0,0 +1,116 @@ +import os +from win32com.client import Dispatch, constants, gencache, DispatchEx +import pythoncom +#import sys + +# sys.coinit_flags = 0 +class PDFConverter: + def __init__(self, pathname, export='.'): + self._handle_postfix = ['doc', 'docx', 'ppt', 'pptx', 'xls', 'xlsx'] + self._filename_list = list() + self._export_folder = os.path.join(os.path.abspath('.'), 'file_server/pdfconver') + if not os.path.exists(self._export_folder): + os.mkdir(self._export_folder) + self._enumerate_filename(pathname) + + def _enumerate_filename(self, pathname): + ''' + 读取所有文件名 + ''' + full_pathname = os.path.abspath(pathname) + if os.path.isfile(full_pathname): + if self._is_legal_postfix(full_pathname): + self._filename_list.append(full_pathname) + else: + raise TypeError('文件 {} 后缀名不合法！仅支持如下文件类型：{}。'.format(pathname, '、'.join(self._handle_postfix))) + elif os.path.isdir(full_pathname): + for relpath, _, files in os.walk(full_pathname): + for name in files: + filename = os.path.join(full_pathname, relpath, name) + if self._is_legal_postfix(filename): + self._filename_list.append(os.path.join(filename)) + else: + raise TypeError('文件/文件夹 {} 不存在或不合法！'.format(pathname)) + + def _is_legal_postfix(self, filename): + return filename.split('.')[-1].lower() in self._handle_postfix and not os.path.basename(filename).startswith( + '~') + + def run_conver(self): + ''' + 进行批量处理，根据后缀名调用函数执行转换 + ''' + print('需要转换的文件数：', len(self._filename_list)) + for filename in self._filename_list: + postfix = filename.split('.')[-1].lower() + funcCall = getattr(self, postfix) + print('原文件：', filename) + funcCall(filename) + print('转换完成！') + + def doc(self, filename): + ''' + doc 和 docx 文件转换 + ''' + name = os.path.basename(filename).split('.')[0] + '.pdf' + exportfile = os.path.join(self._export_folder, name) + print('保存 PDF 文件：', exportfile) + gencache.EnsureModule('{00020905-0000-0000-C000-000000000046}', 0, 8, 4) + pythoncom.CoInitialize() + w = Dispatch("Word.Application") + pythoncom.CoInitialize() # 加上防止 CoInitialize 未加载 + doc = w.Documents.Open(filename) + doc.ExportAsFixedFormat(exportfile, constants.wdExportFormatPDF, + Item=constants.wdExportDocumentWithMarkup, + CreateBookmarks=constants.wdExportCreateHeadingBookmarks) + w.Quit(constants.wdDoNotSaveChanges) + + def docx(self, filename): + self.doc(filename) + def xls(self, filename): + ''' + xls 和 xlsx 文件转换 + ''' + name = os.path.basename(filename).split('.')[0] + '.pdf' + exportfile = os.path.join(self._export_folder, name) + pythoncom.CoInitialize() + xlApp = DispatchEx("Excel.Application") + pythoncom.CoInitialize() + xlApp.Visible = False + xlApp.DisplayAlerts = 0 + books = xlApp.Workbooks.Open(filename, False) + books.ExportAsFixedFormat(0, exportfile) + books.Close(False) + print('保存 PDF 文件：', exportfile) + xlApp.Quit() + + def xlsx(self, filename): + self.xls(filename) + + def ppt(self, filename): + ''' + ppt 和 pptx 文件转换 + ''' + name = os.path.basename(filename).split('.')[0] + '.pdf' + exportfile = os.path.join(self._export_folder, name) + gencache.EnsureModule('{00020905-0000-0000-C000-000000000046}', 0, 8, 4) + pythoncom.CoInitialize() + p = Dispatch("PowerPoint.Application") + pythoncom.CoInitialize() + ppt = p.Presentations.Open(filename, False, False, False) + ppt.ExportAsFixedFormat(exportfile, 2, PrintRange=None) + print('保存 PDF 文件：', exportfile) + p.Quit() + + def pptx(self, filename): + self.ppt(filename) + +if __name__ == "__main__": + # 支持文件夹批量导入 + #folder = 'tmp' + #pathname = os.path.join(os.path.abspath('.'), folder) + # 也支持单个文件的转换 + pathname = "G:/python_study/test.doc" + pdfConverter = PDFConverter(pathname) + pdfConverter.run_conver() + diff --git a/chaoxi/Image_Recogn/main.ipynb b/chaoxi/Image_Recogn/main.ipynb new file mode 100644 index 0000000..730756d --- /dev/null +++ b/chaoxi/Image_Recogn/main.ipynb @@ -0,0 +1,507 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "pycharm": {} + }, + "outputs": [], + "source": [ + "%reload_ext autoreload\n", + "%autoreload 2\n", + "\n", + "import torch\n", + "from torch.utils.data import DataLoader\n", + "from torchvision.datasets import ImageFolder\n", + "from torchvision import transforms as tfs\n", + "from torchvision import models\n", + "from torch import nn\n", + "\n", + "import matplotlib.pyplot as plt\n", + "%matplotlib inline\n", + "\n", + "import os\n", + "os.environ[\"KMP_DUPLICATE_LIB_OK\"]=\"TRUE\"" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "pycharm": {} + }, + "outputs": [], + "source": [ + "# 是否使用GPU\n", + "use_gpu = False" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "pycharm": {} + }, + "outputs": [], + "source": [ + "# 数据增强\n", + "train_transform = tfs.Compose([\n", + " # 训练集的数据预处理\n", + " tfs.Resize([224, 224]),\n", + " tfs.RandomHorizontalFlip(),\n", + " tfs.RandomCrop(128),\n", + " tfs.ToTensor(),\n", + " tfs.Normalize([0.5,0.5,0.5], [0.5,0.5,0.5])\n", + "])\n", + "\n", + "test_transform = tfs.Compose([\n", + " tfs.Resize([224,224]),\n", + "# tfs.RandomCrop(128),\n", + " tfs.ToTensor(),\n", + " tfs.Normalize([0.5,0.5,0.5], [0.5,0.5,0.5])\n", + "])" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "pycharm": {} + }, + "outputs": [], + "source": [ + "# 每一个batch的数据集数目\n", + "batch_size = 10" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "pycharm": {} + }, + "outputs": [], + "source": [ + "# 构建训练集和验证集\n", + "train_set = ImageFolder('./dataset1/train', train_transform)\n", + "train_data = DataLoader(train_set, batch_size, shuffle=True, num_workers=0)\n", + "\n", + "valid_set = ImageFolder('./dataset1/valid', test_transform)\n", + "valid_data = DataLoader(valid_set, 2*batch_size, shuffle=False, num_workers=0)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "pycharm": {} + }, + "outputs": [ + { + "data": { + "text/plain": [ + "{'blank': 0, 'filled': 1, 'other': 2}" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "train_set.class_to_idx" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "pycharm": {} + }, + "outputs": [ + { + "data": { + "text/plain": [ + "14" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "len(valid_data)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "pycharm": {} + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Dataset is ready!\n" + ] + } + ], + "source": [ + "try:\n", + " if iter(train_data).next()[0].shape[0] == batch_size and \\\n", + " iter(valid_data).next()[0].shape[0] == 2*batch_size:\n", + " print('Dataset is ready!')\n", + " else:\n", + " print('Not success, maybe the batch size is wrong')\n", + "except:\n", + " print('not success, image transform is wrong!')" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "pycharm": {} + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "10 3\n", + "Model is ready!\n" + ] + } + ], + "source": [ + "# 构建模型\n", + "def get_model():\n", + " model = models.resnet50(pretrained=True)\n", + " model.fc = nn.Linear(2048, 3)\n", + " return model\n", + "\n", + "try:\n", + " model = get_model()\n", + " with torch.no_grad():\n", + " scorce = model(iter(train_data).next()[0])\n", + " print(scorce.shape[0], scorce.shape[1])\n", + " if scorce.shape[0] == batch_size and scorce.shape[1] == 3:\n", + " print('Model is ready!')\n", + " else:\n", + " print('Model is failed!')\n", + "except:\n", + " print('model is wrong')\n", + "\n", + "if use_gpu:\n", + " model = model.cuda()" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "pycharm": {} + }, + "outputs": [], + "source": [ + "# 构建loss函数和优化器\n", + "criterion = nn.CrossEntropyLoss()\n", + "optimizer = torch.optim.Adam(model.parameters(), lr = 1e-4)\n", + "\n", + "# 训练的epoches数目\n", + "max_epoch = 20" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "pycharm": {} + }, + "outputs": [], + "source": [ + "# 训练\n", + "def train(model, train_data, valid_data, max_epoch, criterion, optimizer):\n", + " freq_print = int(len(train_data) / 3)\n", + " \n", + " metric_log = dict()\n", + " metric_log['train_loss'] = list()\n", + " metric_log['train_acc'] = list()\n", + " if valid_data is not None:\n", + " metric_log['valid_loss'] = list()\n", + " metric_log['valid_acc'] = list()\n", + " \n", + " for e in range(max_epoch):\n", + " model.train()\n", + " running_loss = 0\n", + " running_acc = 0\n", + "\n", + " for i, data in enumerate(train_data, 1):\n", + " img, label = data\n", + " if use_gpu:\n", + " img = img.cuda()\n", + " label = label.cuda()\n", + "\n", + " # forward前向传播\n", + " out = model(img)\n", + "\n", + " # 计算误差\n", + " loss = criterion(out, label.long())\n", + "\n", + " # 反向传播，更新参数\n", + " optimizer.zero_grad()\n", + " loss.backward()\n", + " optimizer.step()\n", + "\n", + " # 计算准确率\n", + " _, pred = out.max(1)\n", + " num_correct = (pred == label.long()).sum().item()\n", + " acc = num_correct/img.shape[0]\n", + "\n", + " running_loss += loss.item()\n", + " running_acc +=acc\n", + "\n", + " if i % freq_print == 0:\n", + " print('[{}]/[{}], train loss: {:.3f}, train acc: {:.3f}' \\\n", + " .format(i, len(train_data), running_loss / i, running_acc / i))\n", + " \n", + " metric_log['train_loss'].append(running_loss / len(train_data))\n", + " metric_log['train_acc'].append(running_acc / len(train_data))\n", + "\n", + " if valid_data is not None:\n", + " model.eval()\n", + " running_loss = 0\n", + " running_acc = 0\n", + " for data in valid_data:\n", + " img, label = data\n", + " if use_gpu:\n", + " img = img.cuda()\n", + " label = label.cuda()\n", + " \n", + " # forward前向传播\n", + " out = model(img)\n", + "\n", + " # 计算误差\n", + " loss = criterion(out, label.long())\n", + "\n", + " # 计算准确度\n", + " _, pred = out.max(1)\n", + " num_correct = (pred==label.long()).sum().item()\n", + " acc = num_correct/img.shape[0]\n", + "\n", + "\n", + " running_loss += loss.item()\n", + " running_acc += acc\n", + "\n", + " metric_log['valid_loss'].append(running_loss/len(valid_data))\n", + " metric_log['valid_acc'].append(running_acc/len(valid_data))\n", + " print_str = 'epoch: {}, train loss: {:.3f}, train acc: {:.3f}, \\\n", + " valid loss: {:.3f}, valid accuracy: {:.3f}'.format(\n", + " e+1, metric_log['train_loss'][-1], metric_log['train_acc'][-1],\n", + " metric_log['valid_loss'][-1], metric_log['valid_acc'][-1])\n", + " else:\n", + " print_str = 'epoch: {}, train loss: {:.3f}, train acc: {:.3f}'.format(\n", + " e+1,\n", + " metric_log['train_loss'][-1],\n", + " metric_log['train_acc'][-1])\n", + " print(print_str)\n", + "\n", + " \n", + " # 可视化\n", + " nrows = 1\n", + " ncols = 2\n", + " figsize= (10, 5)\n", + " _, figs = plt.subplots(nrows, ncols, figsize=figsize)\n", + " if valid_data is not None:\n", + " figs[0].plot(metric_log['train_loss'], label='train loss')\n", + " figs[0].plot(metric_log['valid_loss'], label='valid loss')\n", + " figs[0].axes.set_xlabel('loss')\n", + " figs[0].legend(loc='best')\n", + " figs[1].plot(metric_log['train_acc'], label='train acc')\n", + " figs[1].plot(metric_log['valid_acc'], label='valid acc')\n", + " figs[1].axes.set_xlabel('acc')\n", + " figs[1].legend(loc='best')\n", + " else:\n", + " figs[0].plot(metric_log['train_loss'], label='train loss')\n", + " figs[0].axes.set_xlabel('loss')\n", + " figs[0].legend(loc='best')\n", + " figs[1].plot(metric_log['train_acc'], label='train acc')\n", + " figs[1].axes.set_xlabel('acc')\n", + " figs[1].legend(loc='best')" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "pycharm": {}, + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[9]/[28], train loss: 0.849, train acc: 0.578\n", + "[18]/[28], train loss: 0.643, train acc: 0.694\n", + "[27]/[28], train loss: 0.574, train acc: 0.741\n", + "epoch: 1, train loss: 0.598, train acc: 0.714, valid loss: 0.262, valid accuracy: 0.857\n", + "[9]/[28], train loss: 0.230, train acc: 0.933\n", + "[18]/[28], train loss: 0.227, train acc: 0.928\n", + "[27]/[28], train loss: 0.256, train acc: 0.907\n", + "epoch: 2, train loss: 0.272, train acc: 0.911, valid loss: 0.264, valid accuracy: 0.879\n", + "[9]/[28], train loss: 0.227, train acc: 0.911\n", + "[18]/[28], train loss: 0.319, train acc: 0.872\n", + "[27]/[28], train loss: 0.277, train acc: 0.893\n", + "epoch: 3, train loss: 0.313, train acc: 0.861, valid loss: 0.198, valid accuracy: 0.971\n", + "[9]/[28], train loss: 0.173, train acc: 0.922\n", + "[18]/[28], train loss: 0.225, train acc: 0.900\n", + "[27]/[28], train loss: 0.209, train acc: 0.907\n", + "epoch: 4, train loss: 0.230, train acc: 0.911, valid loss: 0.078, valid accuracy: 0.982\n", + "[9]/[28], train loss: 0.288, train acc: 0.878\n", + "[18]/[28], train loss: 0.262, train acc: 0.906\n", + "[27]/[28], train loss: 0.228, train acc: 0.915\n", + "epoch: 5, train loss: 0.264, train acc: 0.882, valid loss: 0.161, valid accuracy: 0.932\n", + "[9]/[28], train loss: 0.068, train acc: 1.000\n", + "[18]/[28], train loss: 0.116, train acc: 0.978\n", + "[27]/[28], train loss: 0.155, train acc: 0.952\n", + "epoch: 6, train loss: 0.175, train acc: 0.954, valid loss: 0.080, valid accuracy: 1.000\n", + "[9]/[28], train loss: 0.070, train acc: 0.967\n", + "[18]/[28], train loss: 0.174, train acc: 0.922\n", + "[27]/[28], train loss: 0.175, train acc: 0.919\n", + "epoch: 7, train loss: 0.225, train acc: 0.886, valid loss: 0.098, valid accuracy: 0.971\n", + "[9]/[28], train loss: 0.244, train acc: 0.944\n", + "[18]/[28], train loss: 0.199, train acc: 0.956\n", + "[27]/[28], train loss: 0.154, train acc: 0.970\n", + "epoch: 8, train loss: 0.172, train acc: 0.971, valid loss: 0.181, valid accuracy: 0.914\n", + "[9]/[28], train loss: 0.125, train acc: 0.956\n", + "[18]/[28], train loss: 0.139, train acc: 0.961\n", + "[27]/[28], train loss: 0.149, train acc: 0.952\n", + "epoch: 9, train loss: 0.186, train acc: 0.918, valid loss: 0.086, valid accuracy: 0.971\n", + "[9]/[28], train loss: 0.072, train acc: 0.978\n", + "[18]/[28], train loss: 0.097, train acc: 0.972\n", + "[27]/[28], train loss: 0.154, train acc: 0.937\n", + "epoch: 10, train loss: 0.182, train acc: 0.904, valid loss: 0.214, valid accuracy: 0.904\n", + "[9]/[28], train loss: 0.284, train acc: 0.922\n", + "[18]/[28], train loss: 0.198, train acc: 0.939\n", + "[27]/[28], train loss: 0.180, train acc: 0.948\n", + "epoch: 11, train loss: 0.213, train acc: 0.914, valid loss: 0.033, valid accuracy: 0.986\n", + "[9]/[28], train loss: 0.246, train acc: 0.967\n", + "[18]/[28], train loss: 0.169, train acc: 0.972\n", + "[27]/[28], train loss: 0.181, train acc: 0.963\n", + "epoch: 12, train loss: 0.193, train acc: 0.964, valid loss: 0.067, valid accuracy: 0.996\n", + "[9]/[28], train loss: 0.251, train acc: 0.911\n", + "[18]/[28], train loss: 0.177, train acc: 0.939\n", + "[27]/[28], train loss: 0.143, train acc: 0.952\n", + "epoch: 13, train loss: 0.245, train acc: 0.918, valid loss: 0.053, valid accuracy: 1.000\n", + "[9]/[28], train loss: 0.044, train acc: 0.989\n", + "[18]/[28], train loss: 0.033, train acc: 0.989\n", + "[27]/[28], train loss: 0.044, train acc: 0.985\n", + "epoch: 14, train loss: 0.096, train acc: 0.950, valid loss: 0.040, valid accuracy: 0.996\n", + "[9]/[28], train loss: 0.014, train acc: 1.000\n", + "[18]/[28], train loss: 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1.000\n", + "[18]/[28], train loss: 0.027, train acc: 0.994\n", + "[27]/[28], train loss: 0.022, train acc: 0.996\n", + "epoch: 19, train loss: 0.059, train acc: 0.961, valid loss: 0.024, valid accuracy: 1.000\n", + "[9]/[28], train loss: 0.021, train acc: 1.000\n", + "[18]/[28], train loss: 0.015, train acc: 1.000\n", + "[27]/[28], train loss: 0.024, train acc: 0.996\n", + "epoch: 20, train loss: 0.056, train acc: 0.961, valid loss: 0.011, valid accuracy: 1.000\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "

这是详情页

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