def semilogy(x_vals,
y_vals,
x_label,
y_label,
x2_vals=None,
y2_vals=None,
legend=None,
figsize=(3.5, 2.5)):
d2l.set_figsize(figsize)
d2l.plt.xlabel(x_label)
d2l.plt.ylabel(y_label)
d2l.plt.semilogy(x_vals, y_vals)
if x2_vals and y2_vals:
d2l.plt.semilogy(x2_vals, y2_vals, linestyle=':')
d2l.plt.legend(legend)
from d2l import d2lzh as d2l
import mxnet as mx
from mxnet import autograd, gluon, image, init, nd
from mxnet.gluon import data as gdata, loss as gloss, utils as gutils
import sys
import time
d2l.set_figsize()
img = image.imread('./d2l/img/cat1.jpg')
print(img.asnumpy())
d2l.plt.imshow(img.asnumpy())
def show_images(imgs, num_rows, num_cols, scale=2):
figsize = (num_cols * scale, num_rows * scale)
_, axes = d2l.plt.subplots(num_rows, num_cols, figsize=figsize)
for i in range(num_rows):
for j in range(num_cols):
axes[i][j].imshow(imgs[i * num_cols + j].asnumpy())
axes[i][j].axes.get_xaxis().set_visible(False)
axes[i][j].axes.get_yaxis().set_visible(False)
return axes
def apply(img, aug, num_rows=2, num_cols=4, scale=1.5):
Y = [aug(img) for _ in range(num_rows * num_cols)]
show_images(Y, num_rows, num_cols, scale)
#翻转和裁剪
apply(img, gdata.vision.transforms.RandomFlipLeftRight())
img = image.imread('./d2l/img/pikachu.jpg')
feature = image.imresize(img, 256, 256).astype('float32')
X = feature.transpose((2, 0, 1)).expand_dims(axis=0)
def predict(X):
anchors, cls_preds, bbox_preds = net(X.as_in_context(ctx))
cls_probs = cls_preds.softmax().transpose((0, 2, 1))
output = contrib.nd.MultiBoxDetection(cls_probs, bbox_preds, anchors)
idx = [i for i, row in enumerate(output[0]) if row[0].asscalar() != -1]
return output[0, idx]
output = predict(X)
d2l.set_figsize((5, 5))
def display(img, output, threshold):
fig = d2l.plt.imshow(img.asnumpy())
for row in output:
score = row[1].asscalar()
if score < threshold:
continue
h, w = img.shape[0:2]
bbox = [row[2:6] * nd.array((w, h, w, h), ctx=row.context)]
d2l.show_bboxes(fig.axes, bbox, '%.2f' % score, 'w')
display(img, output, threshold=0.3)