This is an automated email from the ASF dual-hosted git repository.
driazati pushed a commit to branch main
in repository https://gitbox.apache.org/repos/asf/tvm.git
The following commit(s) were added to refs/heads/main by this push:
new d31a1fb0db [ci] Dis-allow any non-S3 URLs in CI (#13283)
d31a1fb0db is described below
commit d31a1fb0dbea484dec045c22ce2a756aa1071b38
Author: driazati <[email protected]>
AuthorDate: Fri Dec 2 17:39:15 2022 -0800
[ci] Dis-allow any non-S3 URLs in CI (#13283)
* [ci] Dis-allow any non-S3 URLs in CI
This PR makes it so any URLs accessed in tests in CI must be hosted in
S3. This improves reliability as we've seen even files on GitHub
sometimes serve 503s even when everything else is working fine. This
raises an error if any unallowed URL is detected and adds the remaining
few.
---
tests/python/frontend/darknet/test_forward.py | 200 ++++++++++++++------------
tests/scripts/request_hook/request_hook.py | 161 ++++++++++++++++++++-
2 files changed, 263 insertions(+), 98 deletions(-)
diff --git a/tests/python/frontend/darknet/test_forward.py
b/tests/python/frontend/darknet/test_forward.py
index 5e6af51f32..58695e1fd6 100644
--- a/tests/python/frontend/darknet/test_forward.py
+++ b/tests/python/frontend/darknet/test_forward.py
@@ -34,15 +34,29 @@ from tvm.relay.frontend.darknet import ACTIVATION
from tvm import relay
REPO_URL = "https://github.com/dmlc/web-data/blob/main/darknet/";
-DARKNET_LIB = "libdarknet2.0.so"
-DARKNETLIB_URL = REPO_URL + "lib/" + DARKNET_LIB + "?raw=true"
-LIB = __darknetffi__.dlopen(download_testdata(DARKNETLIB_URL, DARKNET_LIB,
module="darknet"))
-DARKNET_TEST_IMAGE_NAME = "dog.jpg"
-DARKNET_TEST_IMAGE_URL = REPO_URL + "data/" + DARKNET_TEST_IMAGE_NAME +
"?raw=true"
-DARKNET_TEST_IMAGE_PATH = download_testdata(
- DARKNET_TEST_IMAGE_URL, DARKNET_TEST_IMAGE_NAME, module="data"
-)
+# Lazily initialized
+DARKNET_TEST_IMAGE_PATH = None
+LIB = None
+
+
+def _lib():
+ global LIB
+ lib = "libdarknet2.0.so"
+ url = REPO_URL + "lib/" + lib + "?raw=true"
+ if LIB is None:
+ LIB = __darknetffi__.dlopen(download_testdata(url, lib,
module="darknet"))
+
+ return LIB
+
+
+def _darknet_test_image_path():
+ global DARKNET_TEST_IMAGE_PATH
+ if DARKNET_TEST_IMAGE_PATH is None:
+ name = "dog.jpg"
+ url = REPO_URL + "data/" + name + "?raw=true"
+ DARKNET_TEST_IMAGE_PATH = download_testdata(url, name, module="data")
+ return DARKNET_TEST_IMAGE_PATH
def astext(program, unify_free_vars=False):
@@ -96,7 +110,7 @@ def _get_tvm_output(net, data, build_dtype="float32",
states=None):
def _load_net(cfg_url, cfg_name, weights_url, weights_name):
cfg_path = download_testdata(cfg_url, cfg_name, module="darknet")
weights_path = download_testdata(weights_url, weights_name,
module="darknet")
- net = LIB.load_network(cfg_path.encode("utf-8"),
weights_path.encode("utf-8"), 0)
+ net = _lib().load_network(cfg_path.encode("utf-8"),
weights_path.encode("utf-8"), 0)
return net
@@ -104,7 +118,7 @@ def verify_darknet_frontend(net, build_dtype="float32"):
"""Test network with given input image on both darknet and tvm"""
def get_darknet_output(net, img):
- LIB.network_predict_image(net, img)
+ _lib().network_predict_image(net, img)
out = []
for i in range(net.n):
layer = net.layers[i]
@@ -147,8 +161,8 @@ def verify_darknet_frontend(net, build_dtype="float32"):
dtype = "float32"
- img = LIB.letterbox_image(
- LIB.load_image_color(DARKNET_TEST_IMAGE_PATH.encode("utf-8"), 0, 0),
net.w, net.h
+ img = _lib().letterbox_image(
+ _lib().load_image_color(_darknet_test_image_path().encode("utf-8"), 0,
0), net.w, net.h
)
darknet_output = get_darknet_output(net, img)
batch_size = 1
@@ -169,7 +183,7 @@ def _test_rnn_network(net, states):
"""Test network with given input data on both darknet and tvm"""
def get_darknet_network_predict(net, data):
- return LIB.network_predict(net, data)
+ return _lib().network_predict(net, data)
ffi = FFI()
np_arr = np.zeros([1, net.inputs], dtype="float32")
@@ -195,7 +209,7 @@ def test_forward_extraction():
weights_url = "http://pjreddie.com/media/files/"; + weights_name +
"?raw=true"
net = _load_net(cfg_url, cfg_name, weights_url, weights_name)
verify_darknet_frontend(net)
- LIB.free_network(net)
+ _lib().free_network(net)
def test_forward_alexnet():
@@ -207,7 +221,7 @@ def test_forward_alexnet():
weights_url = "http://pjreddie.com/media/files/"; + weights_name +
"?raw=true"
net = _load_net(cfg_url, cfg_name, weights_url, weights_name)
verify_darknet_frontend(net)
- LIB.free_network(net)
+ _lib().free_network(net)
def test_forward_resnet50():
@@ -219,7 +233,7 @@ def test_forward_resnet50():
weights_url = "http://pjreddie.com/media/files/"; + weights_name +
"?raw=true"
net = _load_net(cfg_url, cfg_name, weights_url, weights_name)
verify_darknet_frontend(net)
- LIB.free_network(net)
+ _lib().free_network(net)
def test_forward_resnext50():
@@ -231,7 +245,7 @@ def test_forward_resnext50():
weights_url = "http://pjreddie.com/media/files/"; + weights_name +
"?raw=true"
net = _load_net(cfg_url, cfg_name, weights_url, weights_name)
verify_darknet_frontend(net)
- LIB.free_network(net)
+ _lib().free_network(net)
def test_forward_yolov2():
@@ -244,7 +258,7 @@ def test_forward_yolov2():
net = _load_net(cfg_url, cfg_name, weights_url, weights_name)
build_dtype = {}
verify_darknet_frontend(net, build_dtype)
- LIB.free_network(net)
+ _lib().free_network(net)
def test_forward_yolov3():
@@ -257,88 +271,88 @@ def test_forward_yolov3():
net = _load_net(cfg_url, cfg_name, weights_url, weights_name)
build_dtype = {}
verify_darknet_frontend(net, build_dtype)
- LIB.free_network(net)
+ _lib().free_network(net)
def test_forward_convolutional():
"""test convolutional layer"""
- net = LIB.make_network(1)
- layer = LIB.make_convolutional_layer(1, 224, 224, 3, 32, 1, 3, 2, 0, 1, 0,
0, 0, 0)
+ net = _lib().make_network(1)
+ layer = _lib().make_convolutional_layer(1, 224, 224, 3, 32, 1, 3, 2, 0, 1,
0, 0, 0, 0)
net.layers[0] = layer
net.w = net.h = 224
- LIB.resize_network(net, 224, 224)
+ _lib().resize_network(net, 224, 224)
verify_darknet_frontend(net)
- LIB.free_network(net)
+ _lib().free_network(net)
def test_forward_dense():
"""test fully connected layer"""
- net = LIB.make_network(1)
- layer = LIB.make_connected_layer(1, 75, 20, 1, 0, 0)
+ net = _lib().make_network(1)
+ layer = _lib().make_connected_layer(1, 75, 20, 1, 0, 0)
net.layers[0] = layer
net.w = net.h = 5
- LIB.resize_network(net, 5, 5)
+ _lib().resize_network(net, 5, 5)
verify_darknet_frontend(net)
- LIB.free_network(net)
+ _lib().free_network(net)
def test_forward_dense_batchnorm():
"""test fully connected layer with batchnorm"""
- net = LIB.make_network(1)
- layer = LIB.make_connected_layer(1, 12, 2, 1, 1, 0)
+ net = _lib().make_network(1)
+ layer = _lib().make_connected_layer(1, 12, 2, 1, 1, 0)
for i in range(5):
layer.rolling_mean[i] = np.random.rand(1)
layer.rolling_variance[i] = np.random.rand(1) + 0.5
layer.scales[i] = np.random.rand(1)
net.layers[0] = layer
net.w = net.h = 2
- LIB.resize_network(net, 2, 2)
+ _lib().resize_network(net, 2, 2)
verify_darknet_frontend(net)
- LIB.free_network(net)
+ _lib().free_network(net)
def test_forward_maxpooling():
"""test maxpooling layer"""
- net = LIB.make_network(1)
- layer = LIB.make_maxpool_layer(1, 224, 224, 3, 2, 2, 0)
+ net = _lib().make_network(1)
+ layer = _lib().make_maxpool_layer(1, 224, 224, 3, 2, 2, 0)
net.layers[0] = layer
net.w = net.h = 224
- LIB.resize_network(net, 224, 224)
+ _lib().resize_network(net, 224, 224)
verify_darknet_frontend(net)
- LIB.free_network(net)
+ _lib().free_network(net)
def test_forward_avgpooling():
"""test avgerage pooling layer"""
- net = LIB.make_network(1)
- layer = LIB.make_avgpool_layer(1, 224, 224, 3)
+ net = _lib().make_network(1)
+ layer = _lib().make_avgpool_layer(1, 224, 224, 3)
net.layers[0] = layer
net.w = net.h = 224
- LIB.resize_network(net, 224, 224)
+ _lib().resize_network(net, 224, 224)
verify_darknet_frontend(net)
- LIB.free_network(net)
+ _lib().free_network(net)
def test_forward_conv_batch_norm():
"""test batch normalization layer"""
- net = LIB.make_network(1)
- layer = LIB.make_convolutional_layer(1, 224, 224, 3, 32, 1, 3, 2, 0, 1, 1,
0, 0, 0)
+ net = _lib().make_network(1)
+ layer = _lib().make_convolutional_layer(1, 224, 224, 3, 32, 1, 3, 2, 0, 1,
1, 0, 0, 0)
for i in range(32):
layer.rolling_mean[i] = np.random.rand(1)
layer.rolling_variance[i] = np.random.rand(1) + 0.5
net.layers[0] = layer
net.w = net.h = 224
- LIB.resize_network(net, 224, 224)
+ _lib().resize_network(net, 224, 224)
verify_darknet_frontend(net)
- LIB.free_network(net)
+ _lib().free_network(net)
def test_forward_shortcut():
"""test shortcut layer"""
- net = LIB.make_network(3)
- layer_1 = LIB.make_convolutional_layer(1, 224, 224, 3, 32, 1, 3, 2, 0, 1,
0, 0, 0, 0)
- layer_2 = LIB.make_convolutional_layer(1, 111, 111, 32, 32, 1, 1, 1, 0, 1,
0, 0, 0, 0)
- layer_3 = LIB.make_shortcut_layer(1, 0, 111, 111, 32, 111, 111, 32)
+ net = _lib().make_network(3)
+ layer_1 = _lib().make_convolutional_layer(1, 224, 224, 3, 32, 1, 3, 2, 0,
1, 0, 0, 0, 0)
+ layer_2 = _lib().make_convolutional_layer(1, 111, 111, 32, 32, 1, 1, 1, 0,
1, 0, 0, 0, 0)
+ layer_3 = _lib().make_shortcut_layer(1, 0, 111, 111, 32, 111, 111, 32)
layer_3.activation = ACTIVATION.RELU
layer_3.alpha = 1
layer_3.beta = 1
@@ -346,118 +360,118 @@ def test_forward_shortcut():
net.layers[1] = layer_2
net.layers[2] = layer_3
net.w = net.h = 224
- LIB.resize_network(net, 224, 224)
+ _lib().resize_network(net, 224, 224)
verify_darknet_frontend(net)
- LIB.free_network(net)
+ _lib().free_network(net)
def test_forward_reorg():
"""test reorg layer"""
- net = LIB.make_network(2)
- layer_1 = LIB.make_convolutional_layer(1, 222, 222, 3, 32, 1, 3, 2, 0, 1,
0, 0, 0, 0)
- layer_2 = LIB.make_reorg_layer(1, 110, 110, 32, 2, 0, 0, 0)
+ net = _lib().make_network(2)
+ layer_1 = _lib().make_convolutional_layer(1, 222, 222, 3, 32, 1, 3, 2, 0,
1, 0, 0, 0, 0)
+ layer_2 = _lib().make_reorg_layer(1, 110, 110, 32, 2, 0, 0, 0)
net.layers[0] = layer_1
net.layers[1] = layer_2
net.w = net.h = 222
- LIB.resize_network(net, 222, 222)
+ _lib().resize_network(net, 222, 222)
verify_darknet_frontend(net)
- LIB.free_network(net)
+ _lib().free_network(net)
def test_forward_region():
"""test region layer"""
- net = LIB.make_network(2)
- layer_1 = LIB.make_convolutional_layer(1, 19, 19, 3, 425, 1, 1, 1, 0, 1,
0, 0, 0, 0)
- layer_2 = LIB.make_region_layer(1, 19, 19, 5, 80, 4)
+ net = _lib().make_network(2)
+ layer_1 = _lib().make_convolutional_layer(1, 19, 19, 3, 425, 1, 1, 1, 0,
1, 0, 0, 0, 0)
+ layer_2 = _lib().make_region_layer(1, 19, 19, 5, 80, 4)
layer_2.softmax = 1
net.layers[0] = layer_1
net.layers[1] = layer_2
net.w = net.h = 19
- LIB.resize_network(net, 19, 19)
+ _lib().resize_network(net, 19, 19)
build_dtype = {}
verify_darknet_frontend(net, build_dtype)
- LIB.free_network(net)
+ _lib().free_network(net)
def test_forward_yolo_op():
"""test yolo layer"""
- net = LIB.make_network(2)
- layer_1 = LIB.make_convolutional_layer(1, 224, 224, 3, 14, 1, 3, 2, 0, 1,
0, 0, 0, 0)
- layer_2 = LIB.make_yolo_layer(1, 111, 111, 2, 9, __darknetffi__.NULL, 2)
+ net = _lib().make_network(2)
+ layer_1 = _lib().make_convolutional_layer(1, 224, 224, 3, 14, 1, 3, 2, 0,
1, 0, 0, 0, 0)
+ layer_2 = _lib().make_yolo_layer(1, 111, 111, 2, 9, __darknetffi__.NULL, 2)
net.layers[0] = layer_1
net.layers[1] = layer_2
net.w = net.h = 224
- LIB.resize_network(net, 224, 224)
+ _lib().resize_network(net, 224, 224)
build_dtype = {}
verify_darknet_frontend(net, build_dtype)
- LIB.free_network(net)
+ _lib().free_network(net)
def test_forward_upsample():
"""test upsample layer"""
- net = LIB.make_network(1)
- layer = LIB.make_upsample_layer(1, 19, 19, 3, 3)
+ net = _lib().make_network(1)
+ layer = _lib().make_upsample_layer(1, 19, 19, 3, 3)
layer.scale = 1
net.layers[0] = layer
net.w = net.h = 19
- LIB.resize_network(net, 19, 19)
+ _lib().resize_network(net, 19, 19)
verify_darknet_frontend(net)
- LIB.free_network(net)
+ _lib().free_network(net)
def test_forward_l2normalize():
"""test l2 normalization layer"""
- net = LIB.make_network(1)
- layer = LIB.make_l2norm_layer(1, 224 * 224 * 3)
+ net = _lib().make_network(1)
+ layer = _lib().make_l2norm_layer(1, 224 * 224 * 3)
layer.c = layer.out_c = 3
layer.h = layer.out_h = 224
layer.w = layer.out_w = 224
net.layers[0] = layer
net.w = net.h = 224
- LIB.resize_network(net, 224, 224)
+ _lib().resize_network(net, 224, 224)
verify_darknet_frontend(net)
- LIB.free_network(net)
+ _lib().free_network(net)
def test_forward_elu():
"""test elu activation layer"""
- net = LIB.make_network(1)
- layer_1 = LIB.make_convolutional_layer(1, 224, 224, 3, 32, 1, 3, 2, 0, 1,
0, 0, 0, 0)
+ net = _lib().make_network(1)
+ layer_1 = _lib().make_convolutional_layer(1, 224, 224, 3, 32, 1, 3, 2, 0,
1, 0, 0, 0, 0)
layer_1.activation = ACTIVATION.ELU
net.layers[0] = layer_1
net.w = net.h = 224
- LIB.resize_network(net, 224, 224)
+ _lib().resize_network(net, 224, 224)
verify_darknet_frontend(net)
- LIB.free_network(net)
+ _lib().free_network(net)
def test_forward_softmax():
"""test softmax layer"""
- net = LIB.make_network(1)
- layer_1 = LIB.make_softmax_layer(1, 75, 1)
+ net = _lib().make_network(1)
+ layer_1 = _lib().make_softmax_layer(1, 75, 1)
layer_1.temperature = 1
net.layers[0] = layer_1
net.w = net.h = 5
- LIB.resize_network(net, net.w, net.h)
+ _lib().resize_network(net, net.w, net.h)
verify_darknet_frontend(net)
- LIB.free_network(net)
+ _lib().free_network(net)
def test_forward_softmax_temperature():
"""test softmax layer"""
- net = LIB.make_network(1)
- layer_1 = LIB.make_softmax_layer(1, 75, 1)
+ net = _lib().make_network(1)
+ layer_1 = _lib().make_softmax_layer(1, 75, 1)
layer_1.temperature = 0.8
net.layers[0] = layer_1
net.w = net.h = 5
- LIB.resize_network(net, net.w, net.h)
+ _lib().resize_network(net, net.w, net.h)
verify_darknet_frontend(net)
- LIB.free_network(net)
+ _lib().free_network(net)
def test_forward_activation_logistic():
"""test logistic activation layer"""
- net = LIB.make_network(1)
+ net = _lib().make_network(1)
batch = 1
h = 224
width = 224
@@ -472,7 +486,7 @@ def test_forward_activation_logistic():
binary = 0
xnor = 0
adam = 0
- layer_1 = LIB.make_convolutional_layer(
+ layer_1 = _lib().make_convolutional_layer(
batch,
h,
width,
@@ -491,14 +505,14 @@ def test_forward_activation_logistic():
net.layers[0] = layer_1
net.w = width
net.h = h
- LIB.resize_network(net, net.w, net.h)
+ _lib().resize_network(net, net.w, net.h)
verify_darknet_frontend(net)
- LIB.free_network(net)
+ _lib().free_network(net)
def test_forward_rnn():
"""test RNN layer"""
- net = LIB.make_network(1)
+ net = _lib().make_network(1)
batch = 1
inputs = 4
outputs = 4
@@ -506,15 +520,17 @@ def test_forward_rnn():
activation = ACTIVATION.RELU
batch_normalize = 0
adam = 0
- layer_1 = LIB.make_rnn_layer(batch, inputs, outputs, steps, activation,
batch_normalize, adam)
+ layer_1 = _lib().make_rnn_layer(
+ batch, inputs, outputs, steps, activation, batch_normalize, adam
+ )
net.layers[0] = layer_1
net.inputs = inputs
net.outputs = outputs
net.w = net.h = 0
- LIB.resize_network(net, net.w, net.h)
+ _lib().resize_network(net, net.w, net.h)
states = {"rnn0_state": np.zeros([1, net.inputs])}
_test_rnn_network(net, states)
- LIB.free_network(net)
+ _lib().free_network(net)
if __name__ == "__main__":
diff --git a/tests/scripts/request_hook/request_hook.py
b/tests/scripts/request_hook/request_hook.py
index dd1adf0ded..ce379b6b2c 100644
--- a/tests/scripts/request_hook/request_hook.py
+++ b/tests/scripts/request_hook/request_hook.py
@@ -20,6 +20,8 @@
import urllib.request
import logging
+from urllib.parse import quote
+
LOGGER = None
@@ -30,22 +32,119 @@ URL_MAP = {
"http://dl.caffe.berkeleyvision.org/bvlc_alexnet.caffemodel":
f"{BASE}/bvlc_alexnet.caffemodel",
"http://dl.caffe.berkeleyvision.org/bvlc_googlenet.caffemodel":
f"{BASE}/bvlc_googlenet.caffemodel",
"http://download.tensorflow.org/models/mobilenet_v1_2018_08_02/mobilenet_v1_1.0_224.tgz":
f"{BASE}/tf-mobilenet_v1_1.0_224.tgz",
+
"http://download.tensorflow.org/models/object_detection/ssd_mobilenet_v2_quantized_300x300_coco_2019_01_03.tar.gz":
f"{BASE}/models/object_detection/ssd_mobilenet_v2_quantized_300x300_coco_2019_01_03.tar.gz",
+
"http://download.tensorflow.org/models/tflite_11_05_08/mobilenet_v2_1.0_224.tgz":
f"{BASE}/models/tflite_11_05_08/mobilenet_v2_1.0_224.tgz",
"http://images.cocodataset.org/zips/val2017.zip":
f"{BASE}/cocodataset-val2017.zip",
+ "http://pjreddie.com/media/files/alexnet.weights?raw=true":
f"{BASE}/media/files/alexnet.weights"
+ + quote("?raw=true"),
+ "http://pjreddie.com/media/files/alexnet.weights?raw=true":
f"{BASE}/media/files/alexnet.weights"
+ + quote("?raw=true"),
+ "http://pjreddie.com/media/files/extraction.weights?raw=true":
f"{BASE}/media/files/extraction.weights"
+ + quote("?raw=true"),
+ "http://pjreddie.com/media/files/extraction.weights?raw=true":
f"{BASE}/media/files/extraction.weights"
+ + quote("?raw=true"),
+ "http://pjreddie.com/media/files/resnet50.weights?raw=true":
f"{BASE}/media/files/resnet50.weights"
+ + quote("?raw=true"),
+ "http://pjreddie.com/media/files/resnext50.weights?raw=true":
f"{BASE}/media/files/resnext50.weights"
+ + quote("?raw=true"),
+ "http://pjreddie.com/media/files/yolov2.weights?raw=true":
f"{BASE}/media/files/yolov2.weights"
+ + quote("?raw=true"),
+ "http://pjreddie.com/media/files/yolov3.weights?raw=true":
f"{BASE}/media/files/yolov3.weights"
+ + quote("?raw=true"),
+ "http://www.fit.vutbr.cz/~imikolov/rnnlm/simple-examples.tgz":
f"{BASE}/imikolov/rnnlm/simple-examples.tgz",
"https://bj.bcebos.com/x2paddle/models/paddle_resnet50.tar":
f"{BASE}/bcebos-paddle_resnet50.tar",
"https://data.deepai.org/stanfordcars.zip":
f"{BASE}/deepai-stanfordcars.zip",
+
"https://download.pytorch.org/models/quantized/mobilenet_v2_qnnpack_37f702c5.pth":
f"{BASE}/models/quantized/mobilenet_v2_qnnpack_37f702c5.pth",
+
"https://github.com/ARM-software/ML-zoo/blob/48f458af1e9065d9aad2ad94d24b58d6e7c00817/models/keyword_spotting/ds_cnn_small/tflite_int16/ds_cnn_quantized.tflite?raw=true":
f"{BASE}/ARM-software/ML-zoo/blob/48f458af1e9065d9aad2ad94d24b58d6e7c00817/models/keyword_spotting/ds_cnn_small/tflite_int16/ds_cnn_quantized.tflite"
+ + quote("?raw=true"),
+
"https://raw.githubusercontent.com/tlc-pack/tophub/main/tophub/adreno_v0.01.log":
f"{BASE}/tlc-pack/tophub/main/tophub/adreno_v0.01.log",
"https://docs-assets.developer.apple.com/coreml/models/MobileNet.mlmodel":
f"{BASE}/2022-10-05/MobileNet.mlmodel",
+ "https://docs-assets.developer.apple.com/coreml/models/Resnet50.mlmodel":
f"{BASE}/coreml/models/Resnet50.mlmodel",
+
"https://download.pytorch.org/models/deeplabv3_mobilenet_v3_large-fc3c493d.pth":
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+
"https://storage.googleapis.com/download.tensorflow.org/models/mobilenet_v1_2018_08_02/mobilenet_v1_1.0_224_quant.tgz":
f"{BASE}/download.tensorflow.org/models/mobilenet_v1_2018_08_02/mobilenet_v1_1.0_224_quant.tgz",
+
"https://storage.googleapis.com/download.tensorflow.org/models/mobilenet_v1_2018_08_02/mobilenet_v1_1.0_224.tgz":
f"{BASE}/download.tensorflow.org/models/mobilenet_v1_2018_08_02/mobilenet_v1_1.0_224.tgz",
+
"https://storage.googleapis.com/download.tensorflow.org/models/tflite_11_05_08/inception_v3_quant.tgz":
f"{BASE}/download.tensorflow.org/models/tflite_11_05_08/inception_v3_quant.tgz",
"https://storage.googleapis.com/download.tensorflow.org/models/tflite_11_05_08/mobilenet_v2_1.0_224_quant.tgz":
f"{BASE}/2022-10-05/mobilenet_v2_1.0_224_quant.tgz",
+
"https://storage.googleapis.com/download.tensorflow.org/models/tflite/coco_ssd_mobilenet_v1_1.0_quant_2018_06_29.zip":
f"{BASE}/download.tensorflow.org/models/tflite/coco_ssd_mobilenet_v1_1.0_quant_2018_06_29.zip",
+
"https://storage.googleapis.com/download.tensorflow.org/models/tflite/digit_classifier/mnist.tflite":
f"{BASE}/download.tensorflow.org/models/tflite/digit_classifier/mnist.tflite",
+
"https://storage.googleapis.com/download.tensorflow.org/models/tflite/model_zoo/upload_20180427/inception_v3_2018_04_27.tgz":
f"{BASE}/download.tensorflow.org/models/tflite/model_zoo/upload_20180427/inception_v3_2018_04_27.tgz",
+
"https://storage.googleapis.com/download.tensorflow.org/models/tflite/model_zoo/upload_20180427/inception_v4_2018_04_27.tgz":
f"{BASE}/download.tensorflow.org/models/tflite/model_zoo/upload_20180427/inception_v4_2018_04_27.tgz",
+
"https://storage.googleapis.com/download.tensorflow.org/models/tflite/model_zoo/upload_20180427/squeezenet_2018_04_27.tgz":
f"{BASE}/download.tensorflow.org/models/tflite/model_zoo/upload_20180427/squeezenet_2018_04_27.tgz",
+
"https://storage.googleapis.com/fast-convnets/tflite-models/mbv1_140_90_12b4_720.tflite":
f"{BASE}/fast-convnets/tflite-models/mbv1_140_90_12b4_720.tflite",
+
"https://storage.googleapis.com/fast-convnets/tflite-models/mbv2_200_85_11-16b2_744.tflite":
f"{BASE}/fast-convnets/tflite-models/mbv2_200_85_11-16b2_744.tflite",
+
"https://storage.googleapis.com/mobilenet_v2/checkpoints/mobilenet_v2_1.4_224.tgz":
f"{BASE}/mobilenet_v2/checkpoints/mobilenet_v2_1.4_224.tgz",
+
"https://storage.googleapis.com/mobilenet_v3/checkpoints/v3-large_224_1.0_float.tgz":
f"{BASE}/mobilenet_v3/checkpoints/v3-large_224_1.0_float.tgz",
+
"https://storage.googleapis.com/tensorflow/keras-applications/mobilenet/mobilenet_1_0_224_tf_no_top.h5":
f"{BASE}/tensorflow/keras-applications/mobilenet/mobilenet_1_0_224_tf_no_top.h5",
+
"https://storage.googleapis.com/tensorflow/keras-applications/mobilenet/mobilenet_1_0_224_tf.h5":
f"{BASE}/tensorflow/keras-applications/mobilenet/mobilenet_1_0_224_tf.h5",
"https://storage.googleapis.com/tensorflow/keras-applications/mobilenet/mobilenet_2_5_128_tf.h5":
f"{BASE}/2022-10-05/mobilenet_2_5_128_tf.h5",
-
"https://storage.googleapis.com/tensorflow/keras-applications/resnet/resnet50_weights_tf_dim_ordering_tf_kernels.h5":
f"{BASE}/2022-10-05/resnet50_weights_tf_dim_ordering_tf_kernels.h5",
+
"https://storage.googleapis.com/tensorflow/keras-applications/resnet/resnet50_weights_tf_dim_ordering_tf_kernels.h5":
f"{BASE}/tensorflow/keras-applications/resnet/resnet50_weights_tf_dim_ordering_tf_kernels.h5",
+
"https://storage.googleapis.com/tensorflow/keras-applications/vgg16/vgg16_weights_tf_dim_ordering_tf_kernels.h5":
f"{BASE}/tensorflow/keras-applications/vgg16/vgg16_weights_tf_dim_ordering_tf_kernels.h5",
+
"https://storage.googleapis.com/tensorflow/keras-applications/xception/xception_weights_tf_dim_ordering_tf_kernels.h5":
f"{BASE}/tensorflow/keras-applications/xception/xception_weights_tf_dim_ordering_tf_kernels.h5",
+ "https://storage.googleapis.com/tensorflow/tf-keras-datasets/mnist.npz":
f"{BASE}/tensorflow/tf-keras-datasets/mnist.npz",
}
class TvmRequestHook(urllib.request.Request):
def __init__(self, url, *args, **kwargs):
LOGGER.info(f"Caught access to {url}")
- if url in URL_MAP:
- new_url = URL_MAP[url]
- LOGGER.info(f"Mapped URL {url} to {new_url}")
- else:
- new_url = url
+ url = url.strip()
+ if url not in URL_MAP and not url.startswith(BASE):
+ # Dis-allow any accesses that aren't going through S3
+ msg = (
+ f"Uncaught URL found in CI: {url}. "
+ "A committer must upload the relevant file to S3 via"
+
"https://github.com/apache/tvm/actions/workflows/upload_ci_resource.yml";
+ "and add it to the mapping in
tests/scripts/request_hook/request_hook.py"
+ )
+ raise RuntimeError(msg)
+
+ new_url = URL_MAP[url]
+ LOGGER.info(f"Mapped URL {url} to {new_url}")
super().__init__(new_url, *args, **kwargs)
![https://gist.githubusercontent.com/zhreshold/bcda4716699ac97ea44f791c24310193/raw/fa7ef0e9c9a5daea686d6473a62aacd1a5885849/cat.png":](https://gist.githubusercontent.com/zhreshold/bcda4716699ac97ea44f791c24310193/raw/fa7ef0e9c9a5daea686d6473a62aacd1a5885849/cat.png":){kind=link}
![https://github.com/dmlc/mxnet.js/blob/main/data/cat.png?raw=true":](https://github.com/dmlc/mxnet.js/blob/main/data/cat.png?raw=true":){kind=link}
![https://github.com/dmlc/mxnet.js/raw/main/data/cat.png":](https://github.com/dmlc/mxnet.js/raw/main/data/cat.png":){kind=link}
![https://github.com/dmlc/web-data/blob/main/darknet/data/dog.jpg?raw=true":](https://github.com/dmlc/web-data/blob/main/darknet/data/dog.jpg?raw=true":){kind=link}
![https://github.com/dmlc/web-data/blob/main/darknet/data/dog.jpg":](https://github.com/dmlc/web-data/blob/main/darknet/data/dog.jpg":){kind=link}
![https://github.com/dmlc/web-data/blob/main/darknet/data/person.jpg?raw=true":](https://github.com/dmlc/web-data/blob/main/darknet/data/person.jpg?raw=true":){kind=link}
![https://github.com/dmlc/web-data/blob/main/gluoncv/detection/street_small.jpg?raw=true":](https://github.com/dmlc/web-data/blob/main/gluoncv/detection/street_small.jpg?raw=true":){kind=link}
![https://github.com/dmlc/web-data/raw/main/darknet/data/dog.jpg":](https://github.com/dmlc/web-data/raw/main/darknet/data/dog.jpg":){kind=link}
![https://github.com/dmlc/web-data/raw/main/darknet/data/person.jpg":](https://github.com/dmlc/web-data/raw/main/darknet/data/person.jpg":){kind=link}
![https://github.com/dmlc/web-data/raw/main/gluoncv/detection/street_small.jpg":](https://github.com/dmlc/web-data/raw/main/gluoncv/detection/street_small.jpg":){kind=link}
![https://github.com/dmlc/web-data/raw/main/tensorflow/models/InceptionV1/elephant-299.jpg":](https://github.com/dmlc/web-data/raw/main/tensorflow/models/InceptionV1/elephant-299.jpg":){kind=link}
![https://github.com/dmlc/web-data/raw/master/tensorflow/models/InceptionV1/elephant-299.jpg":](https://github.com/dmlc/web-data/raw/master/tensorflow/models/InceptionV1/elephant-299.jpg":){kind=link}
![https://homes.cs.washington.edu/~moreau/media/vta/cat.jpg":](https://homes.cs.washington.edu/~moreau/media/vta/cat.jpg":){kind=link}
![https://raw.githubusercontent.com/dmlc/mxnet.js/main/data/cat.png":](https://raw.githubusercontent.com/dmlc/mxnet.js/main/data/cat.png":){kind=link}
![https://raw.githubusercontent.com/dmlc/web-data/main/darknet/data/dog.jpg":](https://raw.githubusercontent.com/dmlc/web-data/main/darknet/data/dog.jpg":){kind=link}
![https://raw.githubusercontent.com/dmlc/web-data/main/darknet/data/person.jpg":](https://raw.githubusercontent.com/dmlc/web-data/main/darknet/data/person.jpg":){kind=link}
![https://raw.githubusercontent.com/dmlc/web-data/main/gluoncv/detection/street_small.jpg":](https://raw.githubusercontent.com/dmlc/web-data/main/gluoncv/detection/street_small.jpg":){kind=link}
![https://raw.githubusercontent.com/dmlc/web-data/main/tensorflow/models/InceptionV1/elephant-299.jpg":](https://raw.githubusercontent.com/dmlc/web-data/main/tensorflow/models/InceptionV1/elephant-299.jpg":){kind=link}
![https://raw.githubusercontent.com/dmlc/web-data/master/gluoncv/detection/street_small.jpg":](https://raw.githubusercontent.com/dmlc/web-data/master/gluoncv/detection/street_small.jpg":){kind=link}
![https://s3.amazonaws.com/model-server/inputs/kitten.jpg":](https://s3.amazonaws.com/model-server/inputs/kitten.jpg":){kind=link}