beautified ipynb outupt, removed stderr cells
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@@ -42,11 +42,6 @@
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"Downloading training data (300MB), this can take a few minutes the first time...\n"
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]
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},
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{
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"name": "stderr",
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"output_type": "stream",
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"text": []
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},
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{
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"name": "stdout",
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"output_type": "stream",
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@@ -405,11 +400,6 @@
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"outputId": "13c2ada4-79bf-42f7-f2f9-4355c0173fb0"
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},
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"outputs": [
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{
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"name": "stderr",
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"output_type": "stream",
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"text": []
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},
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{
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"name": "stdout",
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"output_type": "stream",
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+4
-12
@@ -116,7 +116,7 @@
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{
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"data": {
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"text/plain": [
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"(inflow_locᵇ=4, xˢ=32, yˢ=40)"
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"(inflow_loc\u1d47=4, x\u02e2=32, y\u02e2=40)"
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]
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},
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"execution_count": 2,
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@@ -253,7 +253,7 @@
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Velocity dimensions: (inflow_locᵇ=4, xˢ=32, yˢ=40, vectorᶜ=x,y)\n"
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"Velocity dimensions: (inflow_loc\u1d47=4, x\u02e2=32, y\u02e2=40, vector\u1d9c=x,y)\n"
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]
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}
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],
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@@ -295,20 +295,12 @@
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"outputId": "93dae39b-1b31-429a-cf81-f615e23bbf7d"
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},
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"outputs": [
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"/Users/thuerey/miniconda3/envs/tf/lib/python3.8/site-packages/IPython/core/interactiveshell.py:3427: SyntaxWarning: Specifying wrt by position is deprecated in phi.math.funcitonal_gradient() and phi.math.jacobian(). Please pass a list or comma-separated string of parameter names.\n",
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" exec(code_obj, self.user_global_ns, self.user_ns)\n"
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]
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},
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Some gradient info: StaggeredGrid[(inflow_locᵇ=4, xˢ=32, yˢ=40, vectorᶜ=x,y), size=\u001b[94m(x=32, y=40)\u001b[0m \u001b[93mint64\u001b[0m, extrapolation=\u001b[94m0\u001b[0m]\n",
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"\u001b[92m(xˢ=31, yˢ=40)\u001b[0m \u001b[94m2.61e-08 ± 8.5e-01\u001b[0m \u001b[37m(-2e+01...1e+01)\u001b[0m\n"
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"Some gradient info: StaggeredGrid[(inflow_loc\u1d47=4, x\u02e2=32, y\u02e2=40, vector\u1d9c=x,y), size=\u001b[94m(x=32, y=40)\u001b[0m \u001b[93mint64\u001b[0m, extrapolation=\u001b[94m0\u001b[0m]\n",
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"\u001b[92m(x\u02e2=31, y\u02e2=40)\u001b[0m \u001b[94m2.61e-08 \u00b1 8.5e-01\u001b[0m \u001b[37m(-2e+01...1e+01)\u001b[0m\n"
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]
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}
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],
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+2
-18
@@ -158,7 +158,7 @@
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"id": "RY1F4kdWPLNG"
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},
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"source": [
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"Also let's get installing / importing all the necessary libraries out of the way. And while we're at it, we set the random seed - obviously, 42 is the ultimate choice here 🙂"
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"Also let's get installing / importing all the necessary libraries out of the way. And while we're at it, we set the random seed - obviously, 42 is the ultimate choice here \ud83d\ude42"
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]
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},
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{
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@@ -622,21 +622,6 @@
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"outputId": "9e95aa79-6124-4e30-baf1-28bc37801b0f"
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},
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"outputs": [
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"<ipython-input-10-594cd5e5046a>:10: FutureWarning: Domain (phi.physics._boundaries) is deprecated and will be removed in a future release.\n",
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"Please create grids directly, replacing the domain with a dict, e.g.\n",
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" domain = dict(x=64, y=128, bounds=Box(x=1, y=1))\n",
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" grid = CenteredGrid(0, **domain)\n",
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" from phi.physics._boundaries import Domain, OPEN, STICKY as CLOSED\n",
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"<ipython-input-10-594cd5e5046a>:17: DeprecationWarning: Domain is deprecated and will be removed in a future release. Use a dict instead, e.g. CenteredGrid(values, extrapolation, **domain_dict)\n",
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" domain = phi.physics._boundaries.Domain(y=source_res[0], x=source_res[1], boundaries=boundary_conditions, bounds=Box(y=2*simulation_length, x=simulation_length))\n",
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"<ipython-input-10-594cd5e5046a>:17: FutureWarning: Domain is deprecated and will be removed in a future release. Use a dict instead, e.g. CenteredGrid(values, extrapolation, **domain_dict)\n",
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" domain = phi.physics._boundaries.Domain(y=source_res[0], x=source_res[1], boundaries=boundary_conditions, bounds=Box(y=2*simulation_length, x=simulation_length))\n"
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]
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},
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{
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"name": "stdout",
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"output_type": "stream",
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@@ -864,7 +849,6 @@
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"epoch 003/005, batch 001/002, step 0401/0496: loss=\u001b[94m4.7433133\u001b[0m\n",
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"epoch 004/005, batch 001/002, step 0401/0496: loss=\u001b[94m4.522671\u001b[0m\n",
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"epoch 005/005, batch 001/002, step 0401/0496: loss=\u001b[94m2.0179803\u001b[0m\n",
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"WARNING:tensorflow:Compiled the loaded model, but the compiled metrics have yet to be built. `model.compile_metrics` will be empty until you train or evaluate the model.\n",
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"Training done, saved NN\n"
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]
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}
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@@ -997,7 +981,7 @@
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Reynolds numbers in test data set: \u001b[94m(120000.000, 480000.000, 1920000.000, 7680000.000)\u001b[0m along \u001b[92mbatchᵇ\u001b[0m\n"
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"Reynolds numbers in test data set: \u001b[94m(120000.000, 480000.000, 1920000.000, 7680000.000)\u001b[0m along \u001b[92mbatch\u1d47\u001b[0m\n"
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]
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}
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],
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+27
-3
@@ -21,11 +21,15 @@ fileList = [
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]
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#fileList = [ "physgrad-code.ipynb"] # debug, only 1 file
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#fileList = [ "t1.ipynb" ] # debug
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# main
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for fnOut in fileList:
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if not os.path.isfile(fnOut):
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print("Error: "+fnOut+" not found!"); exit(1)
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# create backups
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fn0 = fnOut[:-5] + "bak"
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fn = fn0 + "0"; cnt = 0
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@@ -47,6 +51,7 @@ for fnOut in fileList:
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#print(d.keys()) #print(d["cells"][0].keys())
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# remove TF / pytorch warnings, build list of regular expressions to search for
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# double check, redundant with removing stderr cells (cf delE)
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res = []
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res.append( re.compile(r"WARNING:tensorflow:") )
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res.append( re.compile(r"UserWarning:") )
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@@ -90,16 +95,26 @@ for fnOut in fileList:
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deletes = deletes+1
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#print( d[t][i]["source"][j] +"\n >>> \n" +d2 )
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delE = [] # collect whole entries (sections) to delete
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#print(len( d[t][i]["outputs"] ))
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for j in range(len( d[t][i]["outputs"] )):
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#print(type( d[t][i]["outputs"][j] ))
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#print( d[t][i]["outputs"][j].keys() )
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# search for error stderr cells
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if d[t][i]["outputs"][j]["output_type"]=="stream":
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#print("output j name: "+ format( d[t][i]["outputs"][j]["name"] ) )
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#print("output j: "+ format( d[t][i]["outputs"][j] ) )
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if d[t][i]["outputs"][j]["name"]=="stderr":
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print("stderr found! len text "+ format(len( d[t][i]["outputs"][j]["text"]) ) +", removing entry "+format(j) )
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delE.append(j) # remove the whole stderr entry
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# images
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if d[t][i]["outputs"][j]["output_type"]=="stream":
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#print("len "+ len( d[t][i]["outputs"][j]["text"] ) )
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#print("len "+ format(len( d[t][i]["outputs"][j]["text"] )) )
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dell = [] # collect entries to delete
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dell = [] # collect lines to delete
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for k in range( len( d[t][i]["outputs"][j]["text"] ) ):
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#print(" tout "+ d[t][i]["outputs"][j]["text"][k] ) # debug , print all lines
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nums = []; all_good = True
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@@ -117,8 +132,17 @@ for fnOut in fileList:
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for dl in dell:
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d[t][i]["outputs"][j]["text"].remove(dl)
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#print("len after "+format( len( d[t][i]["outputs"][j]["text"] )) + " A") # debug
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#print("len after "+format( len( d[t][i]["outputs"][j]["text"] )) + " A") # debug
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# afterwards (potentially remove whole entries)
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# if len(delE)>0:
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# print("len bef "+format( len( d[t][i]["outputs"] )) + " A " + format(delE)) # debug
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for de in delE:
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#print(type(d[t][i]["outputs"])); print(de)
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d[t][i]["outputs"].pop(de) # remove array element
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deletes+=1
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# if len(delE)>0:
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# print("len after "+format( len( d[t][i]["outputs"] )) + " A") # debug
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if deletes==0:
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print("Warning: Nothing found in "+fn+"!")
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@@ -363,11 +363,6 @@
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"outputId": "4d938ed4-484b-46df-d0ca-d2268cb97dd0"
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},
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"outputs": [
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{
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"name": "stderr",
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"output_type": "stream",
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"text": []
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},
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{
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"name": "stdout",
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"output_type": "stream",
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@@ -413,11 +408,6 @@
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}
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},
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"outputs": [
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{
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"name": "stderr",
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"output_type": "stream",
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"text": []
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},
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{
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"name": "stdout",
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"output_type": "stream",
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+2
-65
@@ -75,13 +75,7 @@
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"id": "UI1_mMnNQXrN",
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"outputId": "73046435-964d-4d69-adae-ec7e519ede10"
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},
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"outputs": [
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{
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"name": "stderr",
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"output_type": "stream",
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"text": []
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}
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],
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"outputs": [],
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"source": [
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"import sys; sys.path.append('PDE-Control/src'); sys.path.append('PDE-Control-RL/src')\n",
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"import time, csv, os, shutil\n",
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@@ -144,13 +138,6 @@
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"outputId": "bf634970-0b1d-4202-ef7b-6b6043af3f84"
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},
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"outputs": [
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"Cause: [Errno 2] No such file or directory: '<ipython-input-4-a61f572da032>'\n"
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]
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},
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{
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"name": "stdout",
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"output_type": "stream",
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@@ -355,14 +342,6 @@
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"Storing agent and hyperparameters to disk...\n"
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]
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},
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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" \"See the documentation of nn.Upsample for details.\".format(mode)\n",
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" return torch.max_pool1d(input, kernel_size, stride, padding, dilation, ceil_mode)\n"
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]
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},
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{
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"name": "stdout",
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"output_type": "stream",
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@@ -408,13 +387,6 @@
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"outputId": "00a91082-04c5-48ab-f7be-4debf0f5586a"
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},
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"outputs": [
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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" \"See the documentation of nn.Upsample for details.\".format(mode)\n"
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]
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},
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{
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"data": {
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"image/png": 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truncated
|
||||
@@ -484,14 +456,6 @@
|
||||
"text": [
|
||||
"Could not load resample cuda libraries: CUDA binaries not found at /usr/local/lib/python3.7/dist-packages/phi/tf/cuda/build/resample.so. Run \"python setup.py cuda\" to compile them\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"name": "stderr",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"$ python setup.py tf_cuda\n",
|
||||
"before reinstalling phiflow.\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
@@ -532,11 +496,6 @@
|
||||
"Partition length 32 sequence (from 0 to 32) at frame 16\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"name": "stderr",
|
||||
"output_type": "stream",
|
||||
"text": []
|
||||
},
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
@@ -769,13 +728,6 @@
|
||||
"outputId": "2f22a1da-669e-44b2-cfd7-8d3962bfe208"
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stderr",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
" \"See the documentation of nn.Upsample for details.\".format(mode)\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"data": {
|
||||
"image/png": 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truncated
|
||||
@@ -874,15 +826,7 @@
|
||||
"id": "EP8S7UC5_vQD",
|
||||
"outputId": "2d447412-4b6d-4043-ad47-7d5c437fe03e"
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stderr",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
" \"See the documentation of nn.Upsample for details.\".format(mode)\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"rl_frames, gt_frames, unc_frames = rl_trainer.infer_test_set_frames()\n",
|
||||
"\n",
|
||||
@@ -980,13 +924,6 @@
|
||||
"Sanity check - maximum deviation from target state: 0.000000\n",
|
||||
"Sanity check - maximum deviation from target state: 0.000011\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"name": "stderr",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
" \"See the documentation of nn.Upsample for details.\".format(mode)\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
|
||||
+801
-809
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Load diff
Reference in new issue
Block a user