ValueError: Out of range float values are not JSON compliant error on Heroku, and WSL but not on Windows
ValueError: Out of range float values are not JSON compliant error on Heroku, and WSL but not on Windows
我正在尝试使用 FastAPI 将使用 Tensorflow 创建的 CNN 模型部署到 Heroku。该应用程序 运行 在 Heroku 上运行，但 returns 在尝试进行模型预测时出错。 运行 heroku logs --tail returns 这个：
2022-02-17T03:32:12.426547+00:00 app[web.1]: [2022-02-17 03:32:12 +0000] [10] [ERROR] Exception in ASGI application
2022-02-17T03:32:12.426549+00:00 app[web.1]: Traceback (most recent call last):
2022-02-17T03:32:12.426549+00:00 app[web.1]: File "/app/.heroku/python/lib/python3.9/site-packages/uvicorn/protocols/http/h11_impl.py", line 373, in run_asgi
2022-02-17T03:32:12.426550+00:00 app[web.1]: result = await app(self.scope, self.receive, self.send)
2022-02-17T03:32:12.426550+00:00 app[web.1]: File "/app/.heroku/python/lib/python3.9/site-packages/uvicorn/middleware/proxy_headers.py", line 75, in __call__
2022-02-17T03:32:12.426551+00:00 app[web.1]: return await self.app(scope, receive, send)
2022-02-17T03:32:12.426551+00:00 app[web.1]: File "/app/.heroku/python/lib/python3.9/site-packages/fastapi/applications.py", line 212, in __call__
2022-02-17T03:32:12.426552+00:00 app[web.1]: await super().__call__(scope, receive, send)
2022-02-17T03:32:12.426552+00:00 app[web.1]: File "/app/.heroku/python/lib/python3.9/site-packages/starlette/applications.py", line 112, in __call__
2022-02-17T03:32:12.426552+00:00 app[web.1]: await self.middleware_stack(scope, receive, send)
2022-02-17T03:32:12.426553+00:00 app[web.1]: File "/app/.heroku/python/lib/python3.9/site-packages/starlette/middleware/errors.py", line 181, in __call__
2022-02-17T03:32:12.426553+00:00 app[web.1]: raise exc
2022-02-17T03:32:12.426554+00:00 app[web.1]: File "/app/.heroku/python/lib/python3.9/site-packages/starlette/middleware/errors.py", line 159, in __call__
2022-02-17T03:32:12.426554+00:00 app[web.1]: await self.app(scope, receive, _send)
2022-02-17T03:32:12.426554+00:00 app[web.1]: File "/app/.heroku/python/lib/python3.9/site-packages/starlette/exceptions.py", line 82, in __call__
2022-02-17T03:32:12.426554+00:00 app[web.1]: raise exc
2022-02-17T03:32:12.426555+00:00 app[web.1]: File "/app/.heroku/python/lib/python3.9/site-packages/starlette/exceptions.py", line 71, in __call__
2022-02-17T03:32:12.426555+00:00 app[web.1]: await self.app(scope, receive, sender)
2022-02-17T03:32:12.426555+00:00 app[web.1]: File "/app/.heroku/python/lib/python3.9/site-packages/starlette/routing.py", line 656, in __call__
2022-02-17T03:32:12.426555+00:00 app[web.1]: await route.handle(scope, receive, send)
2022-02-17T03:32:12.426556+00:00 app[web.1]: File "/app/.heroku/python/lib/python3.9/site-packages/starlette/routing.py", line 259, in handle
2022-02-17T03:32:12.426556+00:00 app[web.1]: await self.app(scope, receive, send)
2022-02-17T03:32:12.426556+00:00 app[web.1]: File "/app/.heroku/python/lib/python3.9/site-packages/starlette/routing.py", line 61, in app
2022-02-17T03:32:12.426556+00:00 app[web.1]: response = await func(request)
2022-02-17T03:32:12.426557+00:00 app[web.1]: File "/app/.heroku/python/lib/python3.9/site-packages/fastapi/routing.py", line 250, in app
2022-02-17T03:32:12.426557+00:00 app[web.1]: response = actual_response_class(response_data, **response_args)
2022-02-17T03:32:12.426557+00:00 app[web.1]: File "/app/.heroku/python/lib/python3.9/site-packages/starlette/responses.py", line 49, in __init__
2022-02-17T03:32:12.426558+00:00 app[web.1]: self.body = self.render(content)
2022-02-17T03:32:12.426558+00:00 app[web.1]: File "/app/.heroku/python/lib/python3.9/site-packages/starlette/responses.py", line 174, in render
2022-02-17T03:32:12.426558+00:00 app[web.1]: return json.dumps(
2022-02-17T03:32:12.426559+00:00 app[web.1]: File "/app/.heroku/python/lib/python3.9/json/__init__.py", line 234, in dumps
2022-02-17T03:32:12.426559+00:00 app[web.1]: return cls(
2022-02-17T03:32:12.426559+00:00 app[web.1]: File "/app/.heroku/python/lib/python3.9/json/encoder.py", line 199, in encode
2022-02-17T03:32:12.426560+00:00 app[web.1]: chunks = self.iterencode(o, _one_shot=True)
2022-02-17T03:32:12.426560+00:00 app[web.1]: File "/app/.heroku/python/lib/python3.9/json/encoder.py", line 257, in iterencode
2022-02-17T03:32:12.426560+00:00 app[web.1]: return _iterencode(o, 0)
2022-02-17T03:32:12.426561+00:00 app[web.1]: ValueError: Out of range float values are not JSON compliant
最值得注意的是在最后一行，它说“ValueError：超出范围的浮点值不符合 JSON”。我的 procfile 看起来像这样：
web: gunicorn -w 2 -k uvicorn.workers.UvicornWorker main:app
尝试使用 python -m uvicorn main:app 运行 WSL 上的服务器时，出现此错误
ERROR: Exception in ASGI application
Traceback (most recent call last):
File "/home/eruaro/.local/lib/python3.8/site-packages/uvicorn/protocols/http/httptools_impl.py", line 375, in run_asgi
result = await app(self.scope, self.receive, self.send)
File "/home/eruaro/.local/lib/python3.8/site-packages/uvicorn/middleware/proxy_headers.py", line 75, in __call__
return await self.app(scope, receive, send)
File "/home/eruaro/.local/lib/python3.8/site-packages/fastapi/applications.py", line 212, in __call__
await super().__call__(scope, receive, send)
File "/home/eruaro/.local/lib/python3.8/site-packages/starlette/applications.py", line 112, in __call__
await self.middleware_stack(scope, receive, send)
File "/home/eruaro/.local/lib/python3.8/site-packages/starlette/middleware/errors.py", line 181, in __call__
raise exc
File "/home/eruaro/.local/lib/python3.8/site-packages/starlette/middleware/errors.py", line 159, in __call__
await self.app(scope, receive, _send)
File "/home/eruaro/.local/lib/python3.8/site-packages/starlette/exceptions.py", line 82, in __call__
raise exc
File "/home/eruaro/.local/lib/python3.8/site-packages/starlette/exceptions.py", line 71, in __call__
await self.app(scope, receive, sender)
File "/home/eruaro/.local/lib/python3.8/site-packages/starlette/routing.py", line 656, in __call__
await route.handle(scope, receive, send)
File "/home/eruaro/.local/lib/python3.8/site-packages/starlette/routing.py", line 259, in handle
await self.app(scope, receive, send)
File "/home/eruaro/.local/lib/python3.8/site-packages/starlette/routing.py", line 61, in app
response = await func(request)
File "/home/eruaro/.local/lib/python3.8/site-packages/fastapi/routing.py", line 250, in app
response = actual_response_class(response_data, **response_args)
File "/home/eruaro/.local/lib/python3.8/site-packages/starlette/responses.py", line 49, in __init__
self.body = self.render(content)
File "/home/eruaro/.local/lib/python3.8/site-packages/starlette/responses.py", line 174, in render
return json.dumps(
File "/usr/lib/python3.8/json/__init__.py", line 234, in dumps
return cls(
File "/usr/lib/python3.8/json/encoder.py", line 199, in encode
chunks = self.iterencode(o, _one_shot=True)
File "/usr/lib/python3.8/json/encoder.py", line 257, in iterencode
return _iterencode(o, 0)
ValueError: Out of range float values are not JSON compliant
它与我在 Heroku 上获得的类似。但是，在 Windows 上，使用与我在 WSL 上使用的命令相同的命令，该应用程序可以运行。没有返回错误，我可以在服务器上进行预测。如何删除 JSON 合规错误？
作为参考，我的代码在单个 main.py 文件中：
from fastapi import FastAPI
from tensorflow.keras.models import load_model
from tensorflow.keras.utils import get_file
from tensorflow.keras.utils import load_img
from tensorflow.keras.utils import img_to_array
from tensorflow import expand_dims
from tensorflow.nn import softmax
from numpy import argmax
from numpy import max
from numpy import array
app = FastAPI()
model_dir = "food-vision-model.h5"
model = load_model(model_dir)
class_predictions = array([
'apple_pie',
'baby_back_ribs',
'baklava',
'beef_carpaccio',
'beef_tartare',
'beet_salad',
'beignets',
'bibimbap',
'bread_pudding',
'breakfast_burrito',
'bruschetta',
'caesar_salad',
'cannoli',
'caprese_salad',
'carrot_cake',
'ceviche',
'cheesecake',
'cheese_plate',
'chicken_curry',
'chicken_quesadilla',
'chicken_wings',
'chocolate_cake',
'chocolate_mousse',
'churros',
'clam_chowder',
'club_sandwich',
'crab_cakes',
'creme_brulee',
'croque_madame',
'cup_cakes',
'deviled_eggs',
'donuts',
'dumplings',
'edamame',
'eggs_benedict',
'escargots',
'falafel',
'filet_mignon',
'fish_and_chips',
'foie_gras',
'french_fries',
'french_onion_soup',
'french_toast',
'fried_calamari',
'fried_rice',
'frozen_yogurt',
'garlic_bread',
'gnocchi',
'greek_salad',
'grilled_cheese_sandwich',
'grilled_salmon',
'guacamole',
'gyoza',
'hamburger',
'hot_and_sour_soup',
'hot_dog',
'huevos_rancheros',
'hummus',
'ice_cream',
'lasagna',
'lobster_bisque',
'lobster_roll_sandwich',
'macaroni_and_cheese',
'macarons',
'miso_soup',
'mussels',
'nachos',
'omelette',
'onion_rings',
'oysters',
'pad_thai',
'paella',
'pancakes',
'panna_cotta',
'peking_duck',
'pho',
'pizza',
'pork_chop',
'poutine',
'prime_rib',
'pulled_pork_sandwich',
'ramen',
'ravioli',
'red_velvet_cake',
'risotto',
'samosa',
'sashimi',
'scallops',
'seaweed_salad',
'shrimp_and_grits',
'spaghetti_bolognese',
'spaghetti_carbonara',
'spring_rolls',
'steak',
'strawberry_shortcake',
'sushi',
'tacos',
'takoyaki',
'tiramisu',
'tuna_tartare',
'waffles'
])
@app.get("/")
async def root():
return {"message": "Welcome to the Food Vision API!"}
@app.post("/net/image/prediction/")
async def get_net_image_prediction(image_link: str = ""):
if image_link == "":
return {"message": "No image link provided"}
img_path = get_file(
origin = image_link
)
img = load_img(
img_path,
target_size = (224, 224)
)
img_array = img_to_array(img)
img_array = expand_dims(img_array, 0)
pred = model.predict(img_array)
score = softmax(pred[0])
class_prediction = class_predictions[argmax(score)]
model_score = round(max(score) * 100, 2)
return {
"model_prediction_class": class_prediction,
"model_prediction_score": model_score
}
我已经解决了这个问题，解决方案是 docker 化整个应用程序，然后将其部署到 Heroku。这样它就可以在 WSL (Linux) 上 运行 时工作，并且在 Heroku 上也可以扩展（使用 Linux）。
因此需要稍微编辑 main.py 文件：
from fastapi import FastAPI
from tensorflow.keras.models import load_model
from tensorflow.keras.utils import get_file
from tensorflow.keras.utils import load_img
from tensorflow.keras.utils import img_to_array
from tensorflow import expand_dims
from tensorflow.nn import softmax
from numpy import argmax
from numpy import max
from numpy import array
from json import dumps
from uvicorn import run
import os
app = FastAPI()
model_dir = "food-vision-model.h5"
model = load_model(model_dir)
class_predictions = array([
'apple_pie',
'baby_back_ribs',
'baklava',
'beef_carpaccio',
'beef_tartare',
'beet_salad',
'beignets',
'bibimbap',
'bread_pudding',
'breakfast_burrito',
'bruschetta',
'caesar_salad',
'cannoli',
'caprese_salad',
'carrot_cake',
'ceviche',
'cheesecake',
'cheese_plate',
'chicken_curry',
'chicken_quesadilla',
'chicken_wings',
'chocolate_cake',
'chocolate_mousse',
'churros',
'clam_chowder',
'club_sandwich',
'crab_cakes',
'creme_brulee',
'croque_madame',
'cup_cakes',
'deviled_eggs',
'donuts',
'dumplings',
'edamame',
'eggs_benedict',
'escargots',
'falafel',
'filet_mignon',
'fish_and_chips',
'foie_gras',
'french_fries',
'french_onion_soup',
'french_toast',
'fried_calamari',
'fried_rice',
'frozen_yogurt',
'garlic_bread',
'gnocchi',
'greek_salad',
'grilled_cheese_sandwich',
'grilled_salmon',
'guacamole',
'gyoza',
'hamburger',
'hot_and_sour_soup',
'hot_dog',
'huevos_rancheros',
'hummus',
'ice_cream',
'lasagna',
'lobster_bisque',
'lobster_roll_sandwich',
'macaroni_and_cheese',
'macarons',
'miso_soup',
'mussels',
'nachos',
'omelette',
'onion_rings',
'oysters',
'pad_thai',
'paella',
'pancakes',
'panna_cotta',
'peking_duck',
'pho',
'pizza',
'pork_chop',
'poutine',
'prime_rib',
'pulled_pork_sandwich',
'ramen',
'ravioli',
'red_velvet_cake',
'risotto',
'samosa',
'sashimi',
'scallops',
'seaweed_salad',
'shrimp_and_grits',
'spaghetti_bolognese',
'spaghetti_carbonara',
'spring_rolls',
'steak',
'strawberry_shortcake',
'sushi',
'tacos',
'takoyaki',
'tiramisu',
'tuna_tartare',
'waffles'
])
@app.get("/")
async def root():
return {"message": "Welcome to the Food Vision API!"}
@app.post("/net/image/prediction/")
async def get_net_image_prediction(image_link: str = ""):
if image_link == "":
return {"message": "No image link provided"}
img_path = get_file(
origin = image_link
)
img = load_img(
img_path,
target_size = (224, 224)
)
img_array = img_to_array(img)
img_array = expand_dims(img_array, 0)
pred = model.predict(img_array)
score = softmax(pred[0])
class_prediction = class_predictions[argmax(score)]
model_score = round(max(score) * 100, 2)
model_score = dumps(model_score.tolist())
return {
"model_prediction_class": class_prediction,
"model_prediction_score": model_score
}
if __name__ == "__main__":
port = int(os.environ.get('PORT', 5000))
run(app, host="0.0.0.0", port=port)
这里的主要区别在于 os.environ.get() 的使用。我发现只需在 Heroku 上手动定义一个端口，例如 port = 5000 returns 和 R10 (boot timeout) error。
从这里，我创建了一个包含以下内容的 Dockerfile：
FROM python:3.7.3-stretch
# Maintainer info
LABEL maintainer="erickson_ruaroii@dlsu.edu.ph"
# Make working directories
RUN mkdir -p /my-directory/project
WORKDIR /my-directory/project
# Upgrade pip with no cache
RUN pip install --no-cache-dir -U pip
# Copy application requirements file to the created working directory
COPY requirements.txt .
# Install application dependencies from the requirements file
RUN pip install -r requirements.txt
# Copy every file in the source folder to the created working directory
COPY . .
# Run the python application
CMD ["python", "main.py"]
与 requirements.txt 有这些：
fastapi==0.73.0
gunicorn==20.1.0
numpy==1.19.5
uvicorn==0.15.0
image==1.5.33
tensorflow-cpu==2.7.0
我使用 tensorflow-cpu 在一个免费帐户上绕过了 Heroku 的 slug 大小和内存限制。
从这里我可以构建 docker 图像并将其部署到 heroku。
$ docker image build -t app-name .
$ heroku create app-name
$ heroku container:push web --app app-name
$ heroku container:release web --app food-vision-api
在本地运行时，我使用了下面的命令：
$ docker run -p 5000:5000 -d app-name
我正在尝试使用 FastAPI 将使用 Tensorflow 创建的 CNN 模型部署到 Heroku。该应用程序 运行 在 Heroku 上运行，但 returns 在尝试进行模型预测时出错。 运行 heroku logs --tail returns 这个：
2022-02-17T03:32:12.426547+00:00 app[web.1]: [2022-02-17 03:32:12 +0000] [10] [ERROR] Exception in ASGI application
2022-02-17T03:32:12.426549+00:00 app[web.1]: Traceback (most recent call last):
2022-02-17T03:32:12.426549+00:00 app[web.1]: File "/app/.heroku/python/lib/python3.9/site-packages/uvicorn/protocols/http/h11_impl.py", line 373, in run_asgi
2022-02-17T03:32:12.426550+00:00 app[web.1]: result = await app(self.scope, self.receive, self.send)
2022-02-17T03:32:12.426550+00:00 app[web.1]: File "/app/.heroku/python/lib/python3.9/site-packages/uvicorn/middleware/proxy_headers.py", line 75, in __call__
2022-02-17T03:32:12.426551+00:00 app[web.1]: return await self.app(scope, receive, send)
2022-02-17T03:32:12.426551+00:00 app[web.1]: File "/app/.heroku/python/lib/python3.9/site-packages/fastapi/applications.py", line 212, in __call__
2022-02-17T03:32:12.426552+00:00 app[web.1]: await super().__call__(scope, receive, send)
2022-02-17T03:32:12.426552+00:00 app[web.1]: File "/app/.heroku/python/lib/python3.9/site-packages/starlette/applications.py", line 112, in __call__
2022-02-17T03:32:12.426552+00:00 app[web.1]: await self.middleware_stack(scope, receive, send)
2022-02-17T03:32:12.426553+00:00 app[web.1]: File "/app/.heroku/python/lib/python3.9/site-packages/starlette/middleware/errors.py", line 181, in __call__
2022-02-17T03:32:12.426553+00:00 app[web.1]: raise exc
2022-02-17T03:32:12.426554+00:00 app[web.1]: File "/app/.heroku/python/lib/python3.9/site-packages/starlette/middleware/errors.py", line 159, in __call__
2022-02-17T03:32:12.426554+00:00 app[web.1]: await self.app(scope, receive, _send)
2022-02-17T03:32:12.426554+00:00 app[web.1]: File "/app/.heroku/python/lib/python3.9/site-packages/starlette/exceptions.py", line 82, in __call__
2022-02-17T03:32:12.426554+00:00 app[web.1]: raise exc
2022-02-17T03:32:12.426555+00:00 app[web.1]: File "/app/.heroku/python/lib/python3.9/site-packages/starlette/exceptions.py", line 71, in __call__
2022-02-17T03:32:12.426555+00:00 app[web.1]: await self.app(scope, receive, sender)
2022-02-17T03:32:12.426555+00:00 app[web.1]: File "/app/.heroku/python/lib/python3.9/site-packages/starlette/routing.py", line 656, in __call__
2022-02-17T03:32:12.426555+00:00 app[web.1]: await route.handle(scope, receive, send)
2022-02-17T03:32:12.426556+00:00 app[web.1]: File "/app/.heroku/python/lib/python3.9/site-packages/starlette/routing.py", line 259, in handle
2022-02-17T03:32:12.426556+00:00 app[web.1]: await self.app(scope, receive, send)
2022-02-17T03:32:12.426556+00:00 app[web.1]: File "/app/.heroku/python/lib/python3.9/site-packages/starlette/routing.py", line 61, in app
2022-02-17T03:32:12.426556+00:00 app[web.1]: response = await func(request)
2022-02-17T03:32:12.426557+00:00 app[web.1]: File "/app/.heroku/python/lib/python3.9/site-packages/fastapi/routing.py", line 250, in app
2022-02-17T03:32:12.426557+00:00 app[web.1]: response = actual_response_class(response_data, **response_args)
2022-02-17T03:32:12.426557+00:00 app[web.1]: File "/app/.heroku/python/lib/python3.9/site-packages/starlette/responses.py", line 49, in __init__
2022-02-17T03:32:12.426558+00:00 app[web.1]: self.body = self.render(content)
2022-02-17T03:32:12.426558+00:00 app[web.1]: File "/app/.heroku/python/lib/python3.9/site-packages/starlette/responses.py", line 174, in render
2022-02-17T03:32:12.426558+00:00 app[web.1]: return json.dumps(
2022-02-17T03:32:12.426559+00:00 app[web.1]: File "/app/.heroku/python/lib/python3.9/json/__init__.py", line 234, in dumps
2022-02-17T03:32:12.426559+00:00 app[web.1]: return cls(
2022-02-17T03:32:12.426559+00:00 app[web.1]: File "/app/.heroku/python/lib/python3.9/json/encoder.py", line 199, in encode
2022-02-17T03:32:12.426560+00:00 app[web.1]: chunks = self.iterencode(o, _one_shot=True)
2022-02-17T03:32:12.426560+00:00 app[web.1]: File "/app/.heroku/python/lib/python3.9/json/encoder.py", line 257, in iterencode
2022-02-17T03:32:12.426560+00:00 app[web.1]: return _iterencode(o, 0)
2022-02-17T03:32:12.426561+00:00 app[web.1]: ValueError: Out of range float values are not JSON compliant
最值得注意的是在最后一行，它说“ValueError：超出范围的浮点值不符合 JSON”。我的 procfile 看起来像这样：
web: gunicorn -w 2 -k uvicorn.workers.UvicornWorker main:app
尝试使用 python -m uvicorn main:app 运行 WSL 上的服务器时，出现此错误
ERROR: Exception in ASGI application
Traceback (most recent call last):
File "/home/eruaro/.local/lib/python3.8/site-packages/uvicorn/protocols/http/httptools_impl.py", line 375, in run_asgi
result = await app(self.scope, self.receive, self.send)
File "/home/eruaro/.local/lib/python3.8/site-packages/uvicorn/middleware/proxy_headers.py", line 75, in __call__
return await self.app(scope, receive, send)
File "/home/eruaro/.local/lib/python3.8/site-packages/fastapi/applications.py", line 212, in __call__
await super().__call__(scope, receive, send)
File "/home/eruaro/.local/lib/python3.8/site-packages/starlette/applications.py", line 112, in __call__
await self.middleware_stack(scope, receive, send)
File "/home/eruaro/.local/lib/python3.8/site-packages/starlette/middleware/errors.py", line 181, in __call__
raise exc
File "/home/eruaro/.local/lib/python3.8/site-packages/starlette/middleware/errors.py", line 159, in __call__
await self.app(scope, receive, _send)
File "/home/eruaro/.local/lib/python3.8/site-packages/starlette/exceptions.py", line 82, in __call__
raise exc
File "/home/eruaro/.local/lib/python3.8/site-packages/starlette/exceptions.py", line 71, in __call__
await self.app(scope, receive, sender)
File "/home/eruaro/.local/lib/python3.8/site-packages/starlette/routing.py", line 656, in __call__
await route.handle(scope, receive, send)
File "/home/eruaro/.local/lib/python3.8/site-packages/starlette/routing.py", line 259, in handle
await self.app(scope, receive, send)
File "/home/eruaro/.local/lib/python3.8/site-packages/starlette/routing.py", line 61, in app
response = await func(request)
File "/home/eruaro/.local/lib/python3.8/site-packages/fastapi/routing.py", line 250, in app
response = actual_response_class(response_data, **response_args)
File "/home/eruaro/.local/lib/python3.8/site-packages/starlette/responses.py", line 49, in __init__
self.body = self.render(content)
File "/home/eruaro/.local/lib/python3.8/site-packages/starlette/responses.py", line 174, in render
return json.dumps(
File "/usr/lib/python3.8/json/__init__.py", line 234, in dumps
return cls(
File "/usr/lib/python3.8/json/encoder.py", line 199, in encode
chunks = self.iterencode(o, _one_shot=True)
File "/usr/lib/python3.8/json/encoder.py", line 257, in iterencode
return _iterencode(o, 0)
ValueError: Out of range float values are not JSON compliant
它与我在 Heroku 上获得的类似。但是，在 Windows 上，使用与我在 WSL 上使用的命令相同的命令，该应用程序可以运行。没有返回错误，我可以在服务器上进行预测。如何删除 JSON 合规错误？
作为参考，我的代码在单个 main.py 文件中：
from fastapi import FastAPI
from tensorflow.keras.models import load_model
from tensorflow.keras.utils import get_file
from tensorflow.keras.utils import load_img
from tensorflow.keras.utils import img_to_array
from tensorflow import expand_dims
from tensorflow.nn import softmax
from numpy import argmax
from numpy import max
from numpy import array
app = FastAPI()
model_dir = "food-vision-model.h5"
model = load_model(model_dir)
class_predictions = array([
'apple_pie',
'baby_back_ribs',
'baklava',
'beef_carpaccio',
'beef_tartare',
'beet_salad',
'beignets',
'bibimbap',
'bread_pudding',
'breakfast_burrito',
'bruschetta',
'caesar_salad',
'cannoli',
'caprese_salad',
'carrot_cake',
'ceviche',
'cheesecake',
'cheese_plate',
'chicken_curry',
'chicken_quesadilla',
'chicken_wings',
'chocolate_cake',
'chocolate_mousse',
'churros',
'clam_chowder',
'club_sandwich',
'crab_cakes',
'creme_brulee',
'croque_madame',
'cup_cakes',
'deviled_eggs',
'donuts',
'dumplings',
'edamame',
'eggs_benedict',
'escargots',
'falafel',
'filet_mignon',
'fish_and_chips',
'foie_gras',
'french_fries',
'french_onion_soup',
'french_toast',
'fried_calamari',
'fried_rice',
'frozen_yogurt',
'garlic_bread',
'gnocchi',
'greek_salad',
'grilled_cheese_sandwich',
'grilled_salmon',
'guacamole',
'gyoza',
'hamburger',
'hot_and_sour_soup',
'hot_dog',
'huevos_rancheros',
'hummus',
'ice_cream',
'lasagna',
'lobster_bisque',
'lobster_roll_sandwich',
'macaroni_and_cheese',
'macarons',
'miso_soup',
'mussels',
'nachos',
'omelette',
'onion_rings',
'oysters',
'pad_thai',
'paella',
'pancakes',
'panna_cotta',
'peking_duck',
'pho',
'pizza',
'pork_chop',
'poutine',
'prime_rib',
'pulled_pork_sandwich',
'ramen',
'ravioli',
'red_velvet_cake',
'risotto',
'samosa',
'sashimi',
'scallops',
'seaweed_salad',
'shrimp_and_grits',
'spaghetti_bolognese',
'spaghetti_carbonara',
'spring_rolls',
'steak',
'strawberry_shortcake',
'sushi',
'tacos',
'takoyaki',
'tiramisu',
'tuna_tartare',
'waffles'
])
@app.get("/")
async def root():
return {"message": "Welcome to the Food Vision API!"}
@app.post("/net/image/prediction/")
async def get_net_image_prediction(image_link: str = ""):
if image_link == "":
return {"message": "No image link provided"}
img_path = get_file(
origin = image_link
)
img = load_img(
img_path,
target_size = (224, 224)
)
img_array = img_to_array(img)
img_array = expand_dims(img_array, 0)
pred = model.predict(img_array)
score = softmax(pred[0])
class_prediction = class_predictions[argmax(score)]
model_score = round(max(score) * 100, 2)
return {
"model_prediction_class": class_prediction,
"model_prediction_score": model_score
}
我已经解决了这个问题，解决方案是 docker 化整个应用程序，然后将其部署到 Heroku。这样它就可以在 WSL (Linux) 上 运行 时工作，并且在 Heroku 上也可以扩展（使用 Linux）。
因此需要稍微编辑 main.py 文件：
from fastapi import FastAPI
from tensorflow.keras.models import load_model
from tensorflow.keras.utils import get_file
from tensorflow.keras.utils import load_img
from tensorflow.keras.utils import img_to_array
from tensorflow import expand_dims
from tensorflow.nn import softmax
from numpy import argmax
from numpy import max
from numpy import array
from json import dumps
from uvicorn import run
import os
app = FastAPI()
model_dir = "food-vision-model.h5"
model = load_model(model_dir)
class_predictions = array([
'apple_pie',
'baby_back_ribs',
'baklava',
'beef_carpaccio',
'beef_tartare',
'beet_salad',
'beignets',
'bibimbap',
'bread_pudding',
'breakfast_burrito',
'bruschetta',
'caesar_salad',
'cannoli',
'caprese_salad',
'carrot_cake',
'ceviche',
'cheesecake',
'cheese_plate',
'chicken_curry',
'chicken_quesadilla',
'chicken_wings',
'chocolate_cake',
'chocolate_mousse',
'churros',
'clam_chowder',
'club_sandwich',
'crab_cakes',
'creme_brulee',
'croque_madame',
'cup_cakes',
'deviled_eggs',
'donuts',
'dumplings',
'edamame',
'eggs_benedict',
'escargots',
'falafel',
'filet_mignon',
'fish_and_chips',
'foie_gras',
'french_fries',
'french_onion_soup',
'french_toast',
'fried_calamari',
'fried_rice',
'frozen_yogurt',
'garlic_bread',
'gnocchi',
'greek_salad',
'grilled_cheese_sandwich',
'grilled_salmon',
'guacamole',
'gyoza',
'hamburger',
'hot_and_sour_soup',
'hot_dog',
'huevos_rancheros',
'hummus',
'ice_cream',
'lasagna',
'lobster_bisque',
'lobster_roll_sandwich',
'macaroni_and_cheese',
'macarons',
'miso_soup',
'mussels',
'nachos',
'omelette',
'onion_rings',
'oysters',
'pad_thai',
'paella',
'pancakes',
'panna_cotta',
'peking_duck',
'pho',
'pizza',
'pork_chop',
'poutine',
'prime_rib',
'pulled_pork_sandwich',
'ramen',
'ravioli',
'red_velvet_cake',
'risotto',
'samosa',
'sashimi',
'scallops',
'seaweed_salad',
'shrimp_and_grits',
'spaghetti_bolognese',
'spaghetti_carbonara',
'spring_rolls',
'steak',
'strawberry_shortcake',
'sushi',
'tacos',
'takoyaki',
'tiramisu',
'tuna_tartare',
'waffles'
])
@app.get("/")
async def root():
return {"message": "Welcome to the Food Vision API!"}
@app.post("/net/image/prediction/")
async def get_net_image_prediction(image_link: str = ""):
if image_link == "":
return {"message": "No image link provided"}
img_path = get_file(
origin = image_link
)
img = load_img(
img_path,
target_size = (224, 224)
)
img_array = img_to_array(img)
img_array = expand_dims(img_array, 0)
pred = model.predict(img_array)
score = softmax(pred[0])
class_prediction = class_predictions[argmax(score)]
model_score = round(max(score) * 100, 2)
model_score = dumps(model_score.tolist())
return {
"model_prediction_class": class_prediction,
"model_prediction_score": model_score
}
if __name__ == "__main__":
port = int(os.environ.get('PORT', 5000))
run(app, host="0.0.0.0", port=port)
这里的主要区别在于 os.environ.get() 的使用。我发现只需在 Heroku 上手动定义一个端口，例如 port = 5000 returns 和 R10 (boot timeout) error。
从这里，我创建了一个包含以下内容的 Dockerfile：
FROM python:3.7.3-stretch
# Maintainer info
LABEL maintainer="erickson_ruaroii@dlsu.edu.ph"
# Make working directories
RUN mkdir -p /my-directory/project
WORKDIR /my-directory/project
# Upgrade pip with no cache
RUN pip install --no-cache-dir -U pip
# Copy application requirements file to the created working directory
COPY requirements.txt .
# Install application dependencies from the requirements file
RUN pip install -r requirements.txt
# Copy every file in the source folder to the created working directory
COPY . .
# Run the python application
CMD ["python", "main.py"]
与 requirements.txt 有这些：
fastapi==0.73.0
gunicorn==20.1.0
numpy==1.19.5
uvicorn==0.15.0
image==1.5.33
tensorflow-cpu==2.7.0
我使用 tensorflow-cpu 在一个免费帐户上绕过了 Heroku 的 slug 大小和内存限制。
从这里我可以构建 docker 图像并将其部署到 heroku。
$ docker image build -t app-name .
$ heroku create app-name
$ heroku container:push web --app app-name
$ heroku container:release web --app food-vision-api
在本地运行时，我使用了下面的命令：
$ docker run -p 5000:5000 -d app-name