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Outils entrainer et évaluer les modèles
2021-02-02 10:31:38 +01:00
{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import torch\n",
"import matplotlib\n",
"%matplotlib notebook\n",
"from matplotlib import pyplot as plt\n",
"import pickle\n",
"import torch.nn as nn\n",
"import numpy as np"
]
},
{
"cell_type": "code",
Final Commit
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"execution_count": 2,
Outils entrainer et évaluer les modèles
2021-02-02 10:31:38 +01:00
"metadata": {},
"outputs": [],
"source": [
"def load_net(file_path): \n",
" pkl_file = open(file_path, 'rb')\n",
" net= pickle.load(pkl_file)\n",
" pkl_file.close()\n",
" return net\n",
"\n",
"def ini():\n",
" kernel=torch.zeros([8,3,3,3])\n",
" array_0=np.array([[1,2,1],[0,0,0],[-1,-2,-1]],dtype='float32')\n",
" array_1=np.array([[2,1,0],[1,0,-1],[0,-1,-2]],dtype='float32')\n",
" array_2=np.array([[1,0,-1],[2,0,-2],[1,0,-1]],dtype='float32')\n",
" array_3=np.array([[0,-1,-2],[1,0,-1],[2,1,0]],dtype='float32')\n",
" array_4=np.array([[-1,-2,-1],[0,0,0],[1,2,1]],dtype='float32')\n",
" array_5=np.array([[-2,-1,0],[-1,0,1],[0,1,2]],dtype='float32')\n",
" array_6=np.array([[-1,0,1],[-2,0,2],[-1,0,1]],dtype='float32')\n",
" array_7=np.array([[0,1,2],[-1,0,1],[-2,-1,0]],dtype='float32')\n",
" for i in range(3):\n",
" kernel[0,i,:]=torch.from_numpy(array_0)\n",
" kernel[1,i,:]=torch.from_numpy(array_1)\n",
" kernel[2,i,:]=torch.from_numpy(array_2)\n",
" kernel[3,i,:]=torch.from_numpy(array_3)\n",
" kernel[4,i,:]=torch.from_numpy(array_4)\n",
" kernel[5,i,:]=torch.from_numpy(array_5)\n",
" kernel[6,i,:]=torch.from_numpy(array_6)\n",
" kernel[7,i,:]=torch.from_numpy(array_7)\n",
" return torch.nn.Parameter(kernel,requires_grad=True) \n",
"\n",
"class Net(nn.Module):\n",
" def __init__(self,frag_size,psize):\n",
" super(Net, self).__init__()\n",
" \n",
" h_fr=frag_size\n",
" w_fr=frag_size\n",
" \n",
" n=int(h_fr/psize) # n*m patches dans le patch d'entrée\n",
" m=int(w_fr/psize)\n",
" \n",
Final Commit
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" self.conv1 = nn.Conv2d(3,4,kernel_size=3,stride=1,padding=1)\n",
Outils entrainer et évaluer les modèles
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" # Si vous souhaitez initialiser Conv1 avec les poids de DeepMatch, exécutez la ligne suivante\n",
" # self.conv1.weight=ini()\n",
" self.Relu = nn.ReLU(inplace=True)\n",
" self.maxpooling=nn.MaxPool2d(3,stride=2, padding=1)\n",
" \n",
" self.shift1=nn.Conv2d(n*m,n*m,kernel_size=3,stride=1,padding=1)\n",
" self.shift1.weight=kernel_shift_ini(n,m)\n",
" self.add1 = nn.Conv2d(n*m,int(n/2)*int(m/2),kernel_size=1,stride=1,padding=0)\n",
" self.add1.weight=kernel_add_ini(n,m)\n",
" \n",
" n=int(n/2)\n",
" m=int(m/2)\n",
" if n>=2 and m>=2:# Si n=m=1，Notre réseau n'a plus besoin de plus de couches pour agréger les cartes de corrélation\n",
" self.shift2=nn.Conv2d(n*m,n*m,kernel_size=3,stride=1,padding=1)\n",
" self.shift2.weight=kernel_shift_ini(n,m)\n",
" self.add2 = nn.Conv2d(n*m,int(n/2)*int(m/2),kernel_size=1,stride=1,padding=0)\n",
" self.add2.weight=kernel_add_ini(n,m)\n",
" \n",
" n=int(n/2)\n",
" m=int(m/2)\n",
" if n>=2 and m>=2:\n",
" self.shift3=nn.Conv2d(n*m,n*m,kernel_size=3,stride=1,padding=1)\n",
" self.shift3.weight=kernel_shift_ini(n,m)\n",
" self.add3 = nn.Conv2d(n*m,int(n/2)*int(m/2),kernel_size=1,stride=1,padding=0)\n",
" self.add3.weight=kernel_add_ini(n,m)\n",
" \n",
" def get_descripteur(self,img,using_cuda):\n",
" # Utilisez Conv1 pour calculer le descripteur,\n",
" descripteur_img=self.Relu(self.conv1(img))\n",
" b,c,h,w=descripteur_img.shape\n",
" couche_constante=0.5*torch.ones([1,1,h,w])\n",
" if using_cuda:\n",
" couche_constante=couche_constante.cuda()\n",
" # Ajouter une couche constante pour éviter la division par 0 lors de la normalisation\n",
" descripteur_img=torch.cat((descripteur_img,couche_constante),1)\n",
" # la normalisation\n",
" descripteur_img_norm=descripteur_img/torch.norm(descripteur_img,dim=1)\n",
" return descripteur_img_norm\n",
" \n",
" def forward(self,img,frag,using_cuda):\n",
" psize=4\n",
" # Utilisez Conv1 pour calculer le descripteur,\n",
" descripteur_input1=self.get_descripteur(img,using_cuda)\n",
" descripteur_input2=self.get_descripteur(frag,using_cuda)\n",
" \n",
" b,c,h,w=frag.shape\n",
" n=int(h/psize)\n",
" m=int(w/psize)\n",
" \n",
" #######################################\n",
" # Calculer la carte de corrélation par convolution pour les n*m patchs plus petit.\n",
" for i in range(n):\n",
" for j in range(m):\n",
" if i==0 and j==0:\n",
" map_corre=F.conv2d(descripteur_input1,get_patch(descripteur_input2,psize,i,j),padding=2)\n",
" else:\n",
" a=F.conv2d(descripteur_input1,get_patch(descripteur_input2,psize,i,j),padding=2)\n",
" map_corre=torch.cat((map_corre,a),1)\n",
" ########################################\n",
" # Étape de polymérisation\n",
" map_corre=self.maxpooling(map_corre)\n",
" map_corre=self.shift1(map_corre)\n",
" map_corre=self.add1(map_corre)\n",
" \n",
" #########################################\n",
" # Répétez l'étape d'agrégation jusqu'à obtenir le graphique de corrélation du patch d'entrée\n",
" n=int(n/2)\n",
" m=int(m/2)\n",
" if n>=2 and m>=2:\n",
" map_corre=self.maxpooling(map_corre)\n",
" map_corre=self.shift2(map_corre)\n",
" map_corre=self.add2(map_corre)\n",
" \n",
" \n",
" n=int(n/2)\n",
" m=int(m/2)\n",
" if n>=2 and m>=2:\n",
" map_corre=self.maxpooling(map_corre)\n",
" map_corre=self.shift3(map_corre)\n",
" map_corre=self.add3(map_corre)\n",
" \n",
" \n",
" b,c,h,w=map_corre.shape\n",
" # Normalisation de la division par maximum\n",
" map_corre=map_corre/(map_corre.max())\n",
" # Normalisation SoftMax\n",
" #map_corre=(F.softmax(map_corre.reshape(1,1,h*w,1),dim=2)).reshape(b,c,h,w)\n",
" return map_corre\n",
"\n",
"def normalize(a):\n",
" return((a - np.min(a))/np.ptp(a))\n",
"\n",
"def carte(w,save_filename,title):\n",
" \n",
" fig,axs = plt.subplots(3,8,figsize=(15,8))\n",
" \n",
" max_ptp = 0\n",
" ref_im = None\n",
" \n",
" for i in range(3):\n",
" for j in range(8):\n",
" im = axs[i,j].imshow(normalize(w[j,i,:,:]))\n",
" \n",
" if i == 0:\n",
" axs[i,j].set_title('Couche {}'.format(j))\n",
" if j == 0:\n",
" axs[i,j].set_ylabel('Channel {}'.format(i+1))\n",
" \n",
" axs[i,j].set_xticks([])\n",
" axs[i,j].set_yticks([])\n",
"\n",
" fig.subplots_adjust(right=0.8)\n",
" cbar_ax = fig.add_axes([0.85, 0.15, 0.05, 0.7])\n",
" fig.colorbar(im, cax=cbar_ax)\n",
" \n",
" \n",
" \n",
Ajout de code et modifications
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" fig.suptitle(\"{}\".format(title),fontsize=16)\n",
Outils entrainer et évaluer les modèles
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" \n",
" if save_filename != None:\n",
" plt.savefig(save_filename)\n",
Final Commit
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" #plt.close()\n",
"\n",
"def carte4(w,save_filename,title):\n",
" \n",
" fig,axs = plt.subplots(3,4,figsize=(15,8))\n",
" \n",
" max_ptp = 0\n",
" ref_im = None\n",
" \n",
" for i in range(3):\n",
" for j in range(4):\n",
" im = axs[i,j].imshow(normalize(w[j,i,:,:]))\n",
" \n",
" if i == 0:\n",
" axs[i,j].set_title('Couche {}'.format(j))\n",
" if j == 0:\n",
" axs[i,j].set_ylabel('Channel {}'.format(i+1))\n",
" \n",
" axs[i,j].set_xticks([])\n",
" axs[i,j].set_yticks([])\n",
"\n",
" fig.subplots_adjust(right=0.8)\n",
" cbar_ax = fig.add_axes([0.85, 0.15, 0.05, 0.7])\n",
" fig.colorbar(im, cax=cbar_ax)\n",
" \n",
" \n",
" \n",
" fig.suptitle(\"{}\".format(title),fontsize=16)\n",
" \n",
" if save_filename != None:\n",
" plt.savefig(save_filename)\n",
" #plt.close()\n",
" \n",
"def carte_32(w,save_filename,title):\n",
" \n",
" fig,axs = plt.subplots(3,4,figsize=(15,8))\n",
" \n",
" max_ptp = 0\n",
" ref_im = None\n",
" \n",
" for i in range(3):\n",
" for j in range(4):\n",
" #im = axs[i,j].imshow(normalize(w[j,i,:,:]))\n",
" im = axs[i,j].imshow(w[j,i,:,:],cmap='coolwarm')\n",
" \n",
" for a in range(3):\n",
" for b in range(3):\n",
" text = axs[i,j].text(b, a, round(w[j, i, a, b],2),\n",
" ha=\"center\", va=\"center\", color=\"w\")\n",
" \n",
" if i == 0:\n",
" axs[i,j].set_title('Couche {}'.format(j))\n",
" if j == 0:\n",
" axs[i,j].set_ylabel('Channel {}'.format(i+1))\n",
" \n",
" axs[i,j].set_xticks([])\n",
" axs[i,j].set_yticks([])\n",
"\n",
" fig.subplots_adjust(right=0.8)\n",
" cbar_ax = fig.add_axes([0.85, 0.15, 0.05, 0.7])\n",
" \n",
" mini,maxi = np.min(w),np.max(w)\n",
" \n",
" cmap = matplotlib.cm.coolwarm\n",
" norm = matplotlib.colors.Normalize(vmin=mini, vmax=maxi)\n",
" \n",
" fig.colorbar(matplotlib.cm.ScalarMappable(norm=norm, cmap=cmap), cax=cbar_ax,orientation='vertical')\n",
" \n",
" \n",
" \n",
" fig.suptitle(\"{}\".format(title),fontsize=16)\n",
" \n",
" if save_filename != None:\n",
" plt.savefig(save_filename)\n",
" #plt.close()"
Outils entrainer et évaluer les modèles
2021-02-02 10:31:38 +01:00
]
},
{
"cell_type": "code",
Final Commit
2021-03-12 12:12:26 +01:00
"execution_count": 4,
"metadata": {
"scrolled": false
},
Outils entrainer et évaluer les modèles
2021-02-02 10:31:38 +01:00
"outputs": [
Final Commit
2021-03-12 12:12:26 +01:00
{
"name": "stdout",
"output_type": "stream",
"text": [
"(8, 3, 3, 3)\n"
]
},
Outils entrainer et évaluer les modèles
2021-02-02 10:31:38 +01:00
{
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" return WebSocket;\n",
" } else if (typeof(MozWebSocket) !== 'undefined') {\n",
" return MozWebSocket;\n",
" } else {\n",
" alert('Your browser does not have WebSocket support. ' +\n",
" 'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
" 'Firefox 4 and 5 are also supported but you ' +\n",
" 'have to enable WebSockets in about:config.');\n",
" };\n",
"}\n",
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" \"This browser does not support binary websocket messages. \" +\n",
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" '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
" 'text-align: center; padding: 3px;\"/>');\n",
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"\n",
" var icon_img = $('<span/>');\n",
" icon_img.addClass('ui-button-icon-primary ui-icon');\n",
" icon_img.addClass(image);\n",
" icon_img.addClass('ui-corner-all');\n",
"\n",
" var tooltip_span = $('<span/>');\n",
" tooltip_span.addClass('ui-button-text');\n",
" tooltip_span.html(tooltip);\n",
"\n",
" button.append(icon_img);\n",
" button.append(tooltip_span);\n",
"\n",
" nav_element.append(button);\n",
" }\n",
"\n",
" var fmt_picker_span = $('<span/>');\n",
"\n",
" var fmt_picker = $('<select/>');\n",
" fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
" fmt_picker_span.append(fmt_picker);\n",
" nav_element.append(fmt_picker_span);\n",
" this.format_dropdown = fmt_picker[0];\n",
"\n",
" for (var ind in mpl.extensions) {\n",
" var fmt = mpl.extensions[ind];\n",
" var option = $(\n",
" '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
" fmt_picker.append(option);\n",
" }\n",
"\n",
" // Add hover states to the ui-buttons\n",
" $( \".ui-button\" ).hover(\n",
" function() { $(this).addClass(\"ui-state-hover\");},\n",
" function() { $(this).removeClass(\"ui-state-hover\");}\n",
" );\n",
"\n",
" var status_bar = $('<span class=\"mpl-message\"/>');\n",
" nav_element.append(status_bar);\n",
" this.message = status_bar[0];\n",
"}\n",
"\n",
"mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
" // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
" // which will in turn request a refresh of the image.\n",
" this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
"}\n",
"\n",
"mpl.figure.prototype.send_message = function(type, properties) {\n",
" properties['type'] = type;\n",
" properties['figure_id'] = this.id;\n",
" this.ws.send(JSON.stringify(properties));\n",
"}\n",
"\n",
"mpl.figure.prototype.send_draw_message = function() {\n",
" if (!this.waiting) {\n",
" this.waiting = true;\n",
" this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
" }\n",
"}\n",
"\n",
"\n",
"mpl.figure.prototype.handle_save = function(fig, msg) {\n",
" var format_dropdown = fig.format_dropdown;\n",
" var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
" fig.ondownload(fig, format);\n",
"}\n",
"\n",
"\n",
"mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
" var size = msg['size'];\n",
" if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
" fig._resize_canvas(size[0], size[1]);\n",
" fig.send_message(\"refresh\", {});\n",
" };\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
" var x0 = msg['x0'] / mpl.ratio;\n",
" var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
" var x1 = msg['x1'] / mpl.ratio;\n",
" var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
" x0 = Math.floor(x0) + 0.5;\n",
" y0 = Math.floor(y0) + 0.5;\n",
" x1 = Math.floor(x1) + 0.5;\n",
" y1 = Math.floor(y1) + 0.5;\n",
" var min_x = Math.min(x0, x1);\n",
" var min_y = Math.min(y0, y1);\n",
" var width = Math.abs(x1 - x0);\n",
" var height = Math.abs(y1 - y0);\n",
"\n",
" fig.rubberband_context.clearRect(\n",
" 0, 0, fig.canvas.width / mpl.ratio, fig.canvas.height / mpl.ratio);\n",
"\n",
" fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
" // Updates the figure title.\n",
" fig.header.textContent = msg['label'];\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
" var cursor = msg['cursor'];\n",
" switch(cursor)\n",
" {\n",
" case 0:\n",
" cursor = 'pointer';\n",
" break;\n",
" case 1:\n",
" cursor = 'default';\n",
" break;\n",
" case 2:\n",
" cursor = 'crosshair';\n",
" break;\n",
" case 3:\n",
" cursor = 'move';\n",
" break;\n",
" }\n",
" fig.rubberband_canvas.style.cursor = cursor;\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_message = function(fig, msg) {\n",
" fig.message.textContent = msg['message'];\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
" // Request the server to send over a new figure.\n",
" fig.send_draw_message();\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
" fig.image_mode = msg['mode'];\n",
"}\n",
"\n",
"mpl.figure.prototype.updated_canvas_event = function() {\n",
" // Called whenever the canvas gets updated.\n",
" this.send_message(\"ack\", {});\n",
"}\n",
"\n",
"// A function to construct a web socket function for onmessage handling.\n",
"// Called in the figure constructor.\n",
"mpl.figure.prototype._make_on_message_function = function(fig) {\n",
" return function socket_on_message(evt) {\n",
" if (evt.data instanceof Blob) {\n",
" /* FIXME: We get \"Resource interpreted as Image but\n",
" * transferred with MIME type text/plain:\" errors on\n",
" * Chrome. But how to set the MIME type? It doesn't seem\n",
" * to be part of the websocket stream */\n",
" evt.data.type = \"image/png\";\n",
"\n",
" /* Free the memory for the previous frames */\n",
" if (fig.imageObj.src) {\n",
" (window.URL || window.webkitURL).revokeObjectURL(\n",
" fig.imageObj.src);\n",
" }\n",
"\n",
" fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
" evt.data);\n",
" fig.updated_canvas_event();\n",
" fig.waiting = false;\n",
" return;\n",
" }\n",
" else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
" fig.imageObj.src = evt.data;\n",
" fig.updated_canvas_event();\n",
" fig.waiting = false;\n",
" return;\n",
" }\n",
"\n",
" var msg = JSON.parse(evt.data);\n",
" var msg_type = msg['type'];\n",
"\n",
" // Call the \"handle_{type}\" callback, which takes\n",
" // the figure and JSON message as its only arguments.\n",
" try {\n",
" var callback = fig[\"handle_\" + msg_type];\n",
" } catch (e) {\n",
" console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
" return;\n",
" }\n",
"\n",
" if (callback) {\n",
" try {\n",
" // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
" callback(fig, msg);\n",
" } catch (e) {\n",
" console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
" }\n",
" }\n",
" };\n",
"}\n",
"\n",
"// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
"mpl.findpos = function(e) {\n",
" //this section is from http://www.quirksmode.org/js/events_properties.html\n",
" var targ;\n",
" if (!e)\n",
" e = window.event;\n",
" if (e.target)\n",
" targ = e.target;\n",
" else if (e.srcElement)\n",
" targ = e.srcElement;\n",
" if (targ.nodeType == 3) // defeat Safari bug\n",
" targ = targ.parentNode;\n",
"\n",
" // jQuery normalizes the pageX and pageY\n",
" // pageX,Y are the mouse positions relative to the document\n",
" // offset() returns the position of the element relative to the document\n",
" var x = e.pageX - $(targ).offset().left;\n",
" var y = e.pageY - $(targ).offset().top;\n",
"\n",
" return {\"x\": x, \"y\": y};\n",
"};\n",
"\n",
"/*\n",
" * return a copy of an object with only non-object keys\n",
" * we need this to avoid circular references\n",
" * http://stackoverflow.com/a/24161582/3208463\n",
" */\n",
"function simpleKeys (original) {\n",
" return Object.keys(original).reduce(function (obj, key) {\n",
" if (typeof original[key] !== 'object')\n",
" obj[key] = original[key]\n",
" return obj;\n",
" }, {});\n",
"}\n",
"\n",
"mpl.figure.prototype.mouse_event = function(event, name) {\n",
" var canvas_pos = mpl.findpos(event)\n",
"\n",
" if (name === 'button_press')\n",
" {\n",
" this.canvas.focus();\n",
" this.canvas_div.focus();\n",
" }\n",
"\n",
" var x = canvas_pos.x * mpl.ratio;\n",
" var y = canvas_pos.y * mpl.ratio;\n",
"\n",
" this.send_message(name, {x: x, y: y, button: event.button,\n",
" step: event.step,\n",
" guiEvent: simpleKeys(event)});\n",
"\n",
" /* This prevents the web browser from automatically changing to\n",
" * the text insertion cursor when the button is pressed. We want\n",
" * to control all of the cursor setting manually through the\n",
" * 'cursor' event from matplotlib */\n",
" event.preventDefault();\n",
" return false;\n",
"}\n",
"\n",
"mpl.figure.prototype._key_event_extra = function(event, name) {\n",
" // Handle any extra behaviour associated with a key event\n",
"}\n",
"\n",
"mpl.figure.prototype.key_event = function(event, name) {\n",
"\n",
" // Prevent repeat events\n",
" if (name == 'key_press')\n",
" {\n",
" if (event.which === this._key)\n",
" return;\n",
" else\n",
" this._key = event.which;\n",
" }\n",
" if (name == 'key_release')\n",
" this._key = null;\n",
"\n",
" var value = '';\n",
" if (event.ctrlKey && event.which != 17)\n",
" value += \"ctrl+\";\n",
" if (event.altKey && event.which != 18)\n",
" value += \"alt+\";\n",
" if (event.shiftKey && event.which != 16)\n",
" value += \"shift+\";\n",
"\n",
" value += 'k';\n",
" value += event.which.toString();\n",
"\n",
" this._key_event_extra(event, name);\n",
"\n",
" this.send_message(name, {key: value,\n",
" guiEvent: simpleKeys(event)});\n",
" return false;\n",
"}\n",
"\n",
"mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
" if (name == 'download') {\n",
" this.handle_save(this, null);\n",
" } else {\n",
" this.send_message(\"toolbar_button\", {name: name});\n",
" }\n",
"};\n",
"\n",
"mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
" this.message.textContent = tooltip;\n",
"};\n",
"mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
"\n",
"mpl.extensions = [\"eps\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\"];\n",
"\n",
"mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
" // Create a \"websocket\"-like object which calls the given IPython comm\n",
" // object with the appropriate methods. Currently this is a non binary\n",
" // socket, so there is still some room for performance tuning.\n",
" var ws = {};\n",
"\n",
" ws.close = function() {\n",
" comm.close()\n",
" };\n",
" ws.send = function(m) {\n",
" //console.log('sending', m);\n",
" comm.send(m);\n",
" };\n",
" // Register the callback with on_msg.\n",
" comm.on_msg(function(msg) {\n",
" //console.log('receiving', msg['content']['data'], msg);\n",
" // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
" ws.onmessage(msg['content']['data'])\n",
" });\n",
" return ws;\n",
"}\n",
"\n",
"mpl.mpl_figure_comm = function(comm, msg) {\n",
" // This is the function which gets called when the mpl process\n",
" // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
"\n",
" var id = msg.content.data.id;\n",
" // Get hold of the div created by the display call when the Comm\n",
" // socket was opened in Python.\n",
" var element = $(\"#\" + id);\n",
" var ws_proxy = comm_websocket_adapter(comm)\n",
"\n",
" function ondownload(figure, format) {\n",
" window.open(figure.imageObj.src);\n",
" }\n",
"\n",
" var fig = new mpl.figure(id, ws_proxy,\n",
" ondownload,\n",
" element.get(0));\n",
"\n",
" // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
" // web socket which is closed, not our websocket->open comm proxy.\n",
" ws_proxy.onopen();\n",
"\n",
" fig.parent_element = element.get(0);\n",
" fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
" if (!fig.cell_info) {\n",
" console.error(\"Failed to find cell for figure\", id, fig);\n",
" return;\n",
" }\n",
"\n",
" var output_index = fig.cell_info[2]\n",
" var cell = fig.cell_info[0];\n",
"\n",
"};\n",
"\n",
"mpl.figure.prototype.handle_close = function(fig, msg) {\n",
" var width = fig.canvas.width/mpl.ratio\n",
" fig.root.unbind('remove')\n",
"\n",
" // Update the output cell to use the data from the current canvas.\n",
" fig.push_to_output();\n",
" var dataURL = fig.canvas.toDataURL();\n",
" // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
" // the notebook keyboard shortcuts fail.\n",
" IPython.keyboard_manager.enable()\n",
" $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
" fig.close_ws(fig, msg);\n",
"}\n",
"\n",
"mpl.figure.prototype.close_ws = function(fig, msg){\n",
" fig.send_message('closing', msg);\n",
" // fig.ws.close()\n",
"}\n",
"\n",
"mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
" // Turn the data on the canvas into data in the output cell.\n",
" var width = this.canvas.width/mpl.ratio\n",
" var dataURL = this.canvas.toDataURL();\n",
" this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
"}\n",
"\n",
"mpl.figure.prototype.updated_canvas_event = function() {\n",
" // Tell IPython that the notebook contents must change.\n",
" IPython.notebook.set_dirty(true);\n",
" this.send_message(\"ack\", {});\n",
" var fig = this;\n",
" // Wait a second, then push the new image to the DOM so\n",
" // that it is saved nicely (might be nice to debounce this).\n",
" setTimeout(function () { fig.push_to_output() }, 1000);\n",
"}\n",
"\n",
"mpl.figure.prototype._init_toolbar = function() {\n",
" var fig = this;\n",
"\n",
" var nav_element = $('<div/>');\n",
" nav_element.attr('style', 'width: 100%');\n",
" this.root.append(nav_element);\n",
"\n",
" // Define a callback function for later on.\n",
" function toolbar_event(event) {\n",
" return fig.toolbar_button_onclick(event['data']);\n",
" }\n",
" function toolbar_mouse_event(event) {\n",
" return fig.toolbar_button_onmouseover(event['data']);\n",
" }\n",
"\n",
" for(var toolbar_ind in mpl.toolbar_items){\n",
" var name = mpl.toolbar_items[toolbar_ind][0];\n",
" var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
" var image = mpl.toolbar_items[toolbar_ind][2];\n",
" var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
"\n",
" if (!name) { continue; };\n",
"\n",
" var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
" button.click(method_name, toolbar_event);\n",
" button.mouseover(tooltip, toolbar_mouse_event);\n",
" nav_element.append(button);\n",
" }\n",
"\n",
" // Add the status bar.\n",
" var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
" nav_element.append(status_bar);\n",
" this.message = status_bar[0];\n",
"\n",
" // Add the close button to the window.\n",
" var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
" var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
" button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
" button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
" buttongrp.append(button);\n",
" var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
" titlebar.prepend(buttongrp);\n",
"}\n",
"\n",
"mpl.figure.prototype._root_extra_style = function(el){\n",
" var fig = this\n",
" el.on(\"remove\", function(){\n",
"\tfig.close_ws(fig, {});\n",
" });\n",
"}\n",
"\n",
"mpl.figure.prototype._canvas_extra_style = function(el){\n",
" // this is important to make the div 'focusable\n",
" el.attr('tabindex', 0)\n",
" // reach out to IPython and tell the keyboard manager to turn it's self\n",
" // off when our div gets focus\n",
"\n",
" // location in version 3\n",
" if (IPython.notebook.keyboard_manager) {\n",
" IPython.notebook.keyboard_manager.register_events(el);\n",
" }\n",
" else {\n",
" // location in version 2\n",
" IPython.keyboard_manager.register_events(el);\n",
" }\n",
"\n",
"}\n",
"\n",
"mpl.figure.prototype._key_event_extra = function(event, name) {\n",
" var manager = IPython.notebook.keyboard_manager;\n",
" if (!manager)\n",
" manager = IPython.keyboard_manager;\n",
"\n",
" // Check for shift+enter\n",
" if (event.shiftKey && event.which == 13) {\n",
" this.canvas_div.blur();\n",
" // select the cell after this one\n",
" var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n",
" IPython.notebook.select(index + 1);\n",
" }\n",
"}\n",
"\n",
"mpl.figure.prototype.handle_save = function(fig, msg) {\n",
" fig.ondownload(fig, null);\n",
"}\n",
"\n",
"\n",
"mpl.find_output_cell = function(html_output) {\n",
" // Return the cell and output element which can be found *uniquely* in the notebook.\n",
" // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
" // IPython event is triggered only after the cells have been serialised, which for\n",
" // our purposes (turning an active figure into a static one), is too late.\n",
" var cells = IPython.notebook.get_cells();\n",
" var ncells = cells.length;\n",
" for (var i=0; i<ncells; i++) {\n",
" var cell = cells[i];\n",
" if (cell.cell_type === 'code'){\n",
" for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
" var data = cell.output_area.outputs[j];\n",
" if (data.data) {\n",
" // IPython >= 3 moved mimebundle to data attribute of output\n",
" data = data.data;\n",
" }\n",
" if (data['text/html'] == html_output) {\n",
" return [cell, data, j];\n",
" }\n",
" }\n",
" }\n",
" }\n",
"}\n",
"\n",
"// Register the function which deals with the matplotlib target/channel.\n",
"// The kernel may be null if the page has been refreshed.\n",
"if (IPython.notebook.kernel != null) {\n",
" IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
"}\n"
],
"text/plain": [
"<IPython.core.display.Javascript object>"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
Final Commit
2021-03-12 12:12:26 +01:00
"<img src=\"data:image/png;base64,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
Outils entrainer et évaluer les modèles
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],
"text/plain": [
"<IPython.core.display.HTML object>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
Final Commit
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"model_filename = './trained_net/net_trainned_SLLShift_E3_03-10_21-02_0007'\n",
Outils entrainer et évaluer les modèles
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"\n",
"net = load_net(model_filename)\n",
"\n",
"w = net.conv1.weight.data.cpu().numpy()\n",
"\n",
Final Commit
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"print(w.shape)\n",
"\n",
"carte4(w,'poids_32_E4.svg',\"Poids du apres entrainement 32x32 4 epochs color_shift\")\n"
Outils entrainer et évaluer les modèles
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]
},
{
"cell_type": "code",
Final Commit
2021-03-12 12:12:26 +01:00
"execution_count": null,
Outils entrainer et évaluer les modèles
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"metadata": {},
Final Commit
2021-03-12 12:12:26 +01:00
"outputs": [],
Outils entrainer et évaluer les modèles
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"source": [
Final Commit
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"w_values_filename = './trained_net/save_weights_NOMB02-18_20-39_0005'\n",
Outils entrainer et évaluer les modèles
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"\n",
"with open(w_values_filename,'rb') as f:\n",
" w_values = pickle.load(f)\n",
"\n",
"#print(w_values[0][0])\n",
"#print(w_values[-1][0])\n",
" \n",
Final Commit
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"carte4(w_values[0],None,'Premiere')\n",
"carte4(w_values[-1],None,'Derniere')\n"
Outils entrainer et évaluer les modèles
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]
},
{
"cell_type": "code",
Final Commit
2021-03-12 12:12:26 +01:00
"execution_count": null,
Outils entrainer et évaluer les modèles
2021-02-02 10:31:38 +01:00
"metadata": {},
Final Commit
2021-03-12 12:12:26 +01:00
"outputs": [],
Outils entrainer et évaluer les modèles
2021-02-02 10:31:38 +01:00
"source": [
Ajout de code et modifications
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"w_values_filename = './trained_net/save_weights_02-03_01-33_0002'\n",
Outils entrainer et évaluer les modèles
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"\n",
"with open(w_values_filename,'rb') as f:\n",
" w_values = pickle.load(f)\n",
"\n",
"save_filename = './results/images_weights/image{:05}.png'\n",
"\n",
"N = len(w_values)\n",
"for i,w in enumerate(w_values):\n",
" print(w.shape)\n",
" print(\"Generating carte {}/{}\".format(i,N))\n",
" carte(w,save_filename.format(i),i*10)\n",
" \n",
"print(\"Done.\")\n"
]
}
],
"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": "3.8.5"
}
},
"nbformat": 4,
"nbformat_minor": 4
}
Reference in a new issue Copy permalink