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03_test copy.py
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03_test copy.py
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#%%
import terrain_set2
from torch.utils.data import DataLoader
import numpy as np
import pandas as pd
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
import torch
import matplotlib.pyplot as plt
from matplotlib.colors import LightSource
from matplotlib import cm
torch.manual_seed(1)
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
n=128
size=128
boundl = 128
def plot_surface(ax, data, cmap, alpha):
meshx, meshy = np.meshgrid(np.linspace(0, size, size), np.linspace(0, size, size))
ls = LightSource(270, 45)
rgb = ls.shade(data, cmap=cmap, vert_exag=0.1, blend_mode='soft')
_ = ax.plot_surface(meshx, meshy, data,
facecolors=rgb, linewidth=0, antialiased=False, shade=False, alpha=alpha)
def plot_boundary(ax, data):
ax.plot(
np.full(size, 0),
np.linspace(0, size-1, size),
data[:,0],
color="red", linewidth=2, zorder=100
)
if boundl<=128:
return
ax.plot(
np.linspace(0, size-1, size),
np.full(size, size),
data[size-1, :],
color="purple", linewidth=2, zorder=100
)
return
ax.plot(
np.full(size, size),
np.linspace(0, size-1, size),
data[:, size-1],
color="purple", linewidth=2, zorder=100
)
ax.plot(
np.linspace(0, size-1, size),
np.full(size, 0),
data[0,:],
color="red", linewidth=2, zorder=100
)
def show(target, out, r=35):
_, ax = plt.subplots(2,2, subplot_kw=dict(projection='3d'), figsize=(10, 10))
ax1, ax2, ax3, ax4 = ax.flatten()
plot_surface(ax1, target, cm.gist_earth, 1.0)
plot_surface(ax2, out, cm.gist_earth, 1.0)
plot_boundary(ax1, target)
plot_boundary(ax2, target)
plot_surface(ax3, target, cm.gist_earth, 1.0)
plot_surface(ax4, out, cm.gist_earth, 1.0)
plot_boundary(ax3, target)
plot_boundary(ax4, target)
ax1.azim = 180+r
ax2.azim = 180+r
ax1.elev= 35
ax2.elev= 35
ax1.set_title('Truth')
ax2.set_title('Model')
ax3.azim = r
ax4.azim = r
ax3.elev= 35
ax4.elev= 35
ax3.set_title('Truth (back)')
ax4.set_title('Model (back)')
plt.show()
#%%
ts = terrain_set2.TerrainSet('data/USGS_1M_10_x43y465_OR_RogueSiskiyouNF_2019_B19.tif',
size=n, stride=8)
tt = terrain_set2.TerrainSet('data/USGS_1M_10_x43y466_OR_RogueSiskiyouNF_2019_B19.tif',
size=n, stride=8)
test = DataLoader(tt, batch_size=256, shuffle=True,
num_workers=2, pin_memory=True, persistent_workers=True, prefetch_factor=4)
#%%
net = torch.load('models/02-%s' % boundl)
net.eval()
net = net.to(device)
#%%
running_loss = 0.0
lossfn = nn.MSELoss()
with torch.no_grad():
for i,data in enumerate(test, 0):
inputs, targets = data
inputs = inputs[:,0:boundl]
outputs = net(inputs.to(device))
loss = lossfn(outputs, targets.to(device))
running_loss += loss.item()
l = running_loss/len(test)
print("test: %.2f" % (l))
# 2 bounds: 54
# 1 bound: 108
#%%
with torch.no_grad():
# 2800
# 2000
# 1700
# 1400
# 25000 - two rivers
# second file
# 1400
# 2500
# 2700
# 2900
# 3300
# 3700
# 4500
# 4700
# 5200 saddle
# 5300 island
# 5500 multiple rivers
#input,target = ts[1400]
#input = input[0:boundl]
#out = net(torch.Tensor([input]).to(device)).cpu().squeeze(1)
#show(target, out[0].numpy(), r=45)
input,target = tt[8300]
input = input[0:boundl]
out = net(torch.Tensor([input]).to(device)).cpu().squeeze(1)
show(target, out[0].numpy(), r=45)