I want to do something like this:
# pytorch_model to train, caffe_model freezed
torch_out = pytorch_model(input)
caffe_out = caffe_model(torch_out)
loss = criterion(caffe_out, label)
loss.backward() # or something like torch_out.backward()
I can easily get the gradient of torch_out provided by caffe_model.backward(), but how can I update pytorch_model with that?
Related
I have created a class in which am loading my custom weights for yolo v5, v7, and v8 to get detection out of this class I am getting xyxy values and class name. now I want to deploy deep SORT on it by using this specific class with no extra files. in my detection class I am simply using torch.hub.load() and access to my basic functions in yolo v5, v7, and v8. now I am looking for specific and straightforward techniques to apply deep sort by using my detection class. is it possible? if it is possible please tell me how can I do it. if it is not possible, what is the simplest method to implement deep sort?
import torch
import cv2
class YoloDetector:
def __init__(self, conf_thold, device, weights_path, expected_objs):
# taking the image file path, confidence level and GPU or CPU selection
self._model = torch.hub.load("WongKinYiu/yolov7", "custom", f"{weights_path}", trust_repo=True)
self._model.conf = conf_thold # NMS confidence threshold
self._model.classes = expected_objs # (optional list) filter by class
self._model.to(device) # specifying device type
def process_image(self, image):
results = self._model(image)
predictions = [] # final list to return all detections
detection = results.pandas().xyxy[0]
for i in range(len(detection)):
# getting bbox and class name one by one
class_name = detection["name"]
xmin = detection["xmin"][i]
ymin = detection["ymin"][i]
xmax = detection["xmax"][i]
ymax = detection["ymax"][i]
# parallely appending the values in list using dictionary
predictions.append({'class': class_name[i], 'bbox': [int(xmin), int(ymin), int(xmax), int(ymax)]})
return predictions
that is the code I am using for getting detections now please tell me how can I implement deep sort by using this.
Here we have an abstract of this problem:
Assuming that we have two models: ResNet and EfficienNet, respectively.
The first model is as follow (ResNet):
def __init__(self, in_channels, out_channels, num_classes):
super().__init__()
self.conv1_0 = _conv3x3(3, 32, stride=2)
self.bn1_0 = _bn(32)
self.conv1_1 = _conv3x3(32, 32, stride=1)
self.bn1_1 = _bn(32)
self.conv1_2 = _conv3x3(32, 64, stride=1)
self.relu = nn.ReLU()
self.pad = torch.nn.ReplicationPad2d(padding=(0, 0, 1, 1))
self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2)
self.end_point = _fc(225, num_classes)
def forward(self, x):
x = self.conv1(x)
#and so on...
out = self.end_point(x)
return out
while the second model has been downloaded as follow (Efficient):
efficientNet = models.efficientnet_b5().to(device)
So, we have two models but the first is developed to scratch and the second one is imported by torchvision.models library.
Now, we want the EfficientNet to take the output of ResNet, then make an end-to-end optimization from EfficientNet’s output to first layer of ResNet.
I know an easier way, which consist of changing the codes and directly using ResNet’s result as an input of Efficient.forward(). However, Efficient model is too complex, making such a change is difficult.
For mathematical reasons, assuming that we have antoher easy model between the ResNet and EfficientNet, that it's called gumbel_model.
So, in conclusion we have 3 models but we got just one target labels about the last one of them, then we can only calculate the loss of the last model (Efficent Net).
When we calculate the loss of the last model, we actually write the three rows as follow:
optimizer.zero_grad()
loss.backforward()
optimizer.step()
Where the optimizer is as follow:
optimizer = optim.SGD([dict(params=efficientNet.parameters(), lr=LR)])
Is it correct that we are backpropagationing only the last model?
To backpropagation end to end models, is it necessary to add parameters of ResNet like the first arg of optim.SGD? If Yes, how could we get params by ResNet? (see above)
I have tried using some codes during one epoch, as follow :
efficientNet = get_efficient_trained(device, out_features=1, in_features=3,path_model)
predictor = ResNet(ResidualBlockBase, layer_config, num_classes=num_cl)
for i, imgs in enumerate(dataloader):
inputs, labels = imgs
inputs, labels = inputs.to(device), labels.to(device)
predictor_output = predictor(inputs)
predictor_gumbel_output = gumbel(predictor_output)
optimizer.zero_grad()
outputs = efficientnet(predictor_gumbel_output).torch.squeeze(outputs, 1)
loss = loss_fn(outputs, labels)
loss.backward()
optimizer.step()
return model
But from the results I have the apprehension that only EfficientNet is just trained.
Are there any ways to let the back-optimization reach to ResNet?
How could I solve this type of problem?*
Waiting for responses.
I really thank you all in advance.
The params argument of the optimizer specifies all the parameters you want to optimize. Here, as you are only passing the parameters of EfficientNet, only those get optimized, as you suspect.
To optimize for all parameters end-to-end, simply pass them all when initializing the optimizer. This can be done as:
optimizer = optim.SGD(list(efficientNet.parameters()) + list(gumbel.parameters()) + list(predictor.parameters()), lr=LR)
I am trying to create a Dropout Layer for my neural network using nn.Sequential() like this:
class DropoutLayer(nn.Module):
def __init__(self, p):
super().__init__()
self.p = p
def forward(self, input):
if self.training:
u1 = (np.random.rand(*input.shape)<self.p)
u1 *= input
return u1
else:
input *= self.p
model = nn.Sequential(Flatten(),DropoutLayer(p = 0.7),nn.LogSoftmax(dim = -1))
opt = torch.optim.Adam(modelDp.parameters(), lr=0.005)
train(modelDp, opt, 5)
But I get this error:
ValueError: optimizer got an empty parameter list
First, there is what I assume to be a small typo : you declare model = nn.Sequential(...) but then use modelDp.parameters(). I assume you just made a small copypaste mistake and these are actually the same thing.
This error is yielded because no layer in your model has trainable parameters, i.e parameters that will be affected by the gradient backpropagation step. Actually, this "network" cannot learn anything at all.
To get rid of the error and get an actual working neural network, you need to include the learning layers, which according to the previous error you had reported, are linear layers. That would be something like :
model = nn.Sequential(nn.Linear(784, 10), Flatten(), DropoutLayer(0.7), nn.LogSoftMax(dim=-1))
Now a couple additional remarks :
You may want to use the pytorch random tensors instead of Numpy's. It will be easier to deal with the devicewhen you will eventually want to move your network on GPU.
This code is going to yield another error as soon as you try it in eval mode because your second conditional branch in the forward method does not return anything. You may want to replace the instruction with return input * self.p
This might be a stupid question as nobody on fastai is trying to answer it. If it is one you can go ahead and tell me but also please tell me the answer, because right now I am completely lost.
I am currently working on a U-Net model for the segmentation of cells in microscopy images. Due to class imbalances and to amplify the importance of cell boundaries, I calculated a pixelwise weightmap for each image that I pass into fastai. Therefore I created a new ItemBase class to save labels and weights together:
class WeightedLabels(ItemBase):
"""
Custom ItemBase to store and process labels and pixelwise weights together.
Also handling the target_size of the labels.
"""
def __init__(self, lbl: Image, wgt: Image, target_size: Tuple = None):
self.lbl, self.wgt = lbl, wgt
self.obj, self.data = (lbl, wgt), [lbl.data, wgt.data]
self.target_size = target_size
...
I use extensive augmentation, like elastic deformation, mirroring and rotations on both weights and labels, as well as the original image. I determine the Loss with a custom Cross-entropy loss function that uses the weights to get the weighted loss for each pixel and averages them.
My problem is, that I do not get a very good performace. I have the feeling that might be because of fastai trying to predict the weights as well. My questions are:
Am I right to assume my model tries to predict both?
If so, how do I tell the learner what to use for updating the layers and to only predict part of my labels, while still applying augmentation to both?
Here's the code for how I implemented my custom LabelList and my custom ItemList:
class CustomSegmentationLabelList(ImageList):
"'Item List' suitable for WeightedLabels containing labels and pixelweights"
_processor = vision.data.SegmentationProcessor
def __init__(self,
items: Iterator,
wghts = None,
classes: Collection = None,
target_size: Tuple = None,
loss_func=CrossEntropyFlat(axis=1),
**kwargs):
super().__init__(items, **kwargs)
self.copy_new.append('classes')
self.copy_new.append('wghts')
self.classes, self.loss_func, self.wghts = classes, loss_func, wghts
self.target_size = target_size
def open(self, fn):
res = io.imread(fn)
res = pil2tensor(res, np.float32)
return Image(res)
def get(self, i):
fn = super().get(i)
wt = self.wghts[i]
return WeightedLabels(fn, self.open(wt), self.target_size)
def reconstruct(self, t: Tensor):
return WeightedLabels(Image(t[0]), Image(t[1]), self.target_size)
class CustomSegmentationItemList(ImageList):
"'ItemList' suitable for segmentation with pixelwise weighted loss"
_label_cls, _square_show_res = CustomSegmentationLabelList, False
def label_from_funcs(self, get_labels: Callable, get_weights: Callable,
label_cls: Callable = None, classes=None,
target_size: Tuple = None, **kwargs) -> 'LabelList':
"Get weights and labels from two functions. Saves them in a CustomSegmentationLabelList"
kwargs = {}
wghts = [get_weights(o) for o in self.items]
labels = [get_labels(o) for o in self.items]
if target_size:
print(
f'Masks will be cropped to {target_size}. Choose \'target_size \\= None \' to keep initial size.')
else:
print(f'Masks will not be cropped.')
y = CustomSegmentationLabelList(
labels, wghts, classes, target_size, path=self.path)
res = self._label_list(x=self, y=y)
return res
Also here the part, where I initiate my databunch object:
data = (CustomSegmentationItemList.from_df(img_df,IMG_PATH, convert_mode=IMAGE_TYPE)
.split_by_rand_pct(valid_pct=(1/N_SPLITS),seed=SEED)
.label_from_funcs(get_labels, get_weights, target_size=MASK_SHAPE, classes = array(['background','cell']))
.transform(tfms=tfms, tfm_y=True)
.databunch(bs=BATCH_SIZE))
I use the regular learner, where I pass in my U-Net model, the data, my loss function and some additional arguments that shouldn't really matter here. When trying to apply my model, after training it, it gives me two identical output tensors. I assume that one is probably for the labels and the other for the weights. Both have the following dimensions: (WxHxC). I do not understand why this is happening, because my model is supposed to only have one output in form of (WxHxC). If this happens during prediction, this probably also happens during training. How can I overcome this?
A Keras introduction to Seq2Seq model have been published a few weeks ago that can be found here. I do not really understand one part of this code:
decoder_lstm = LSTM(latent_dim, return_sequences=True, return_state=True)
decoder_outputs, _, _= decoder_lstm(decoder_inputs,initial_state=encoder_states)
decoder_dense = Dense(num_decoder_tokens, activation='softmax')
decoder_outputs = decoder_dense(decoder_outputs)
Here the decoder_lstm is defined. It is a layer of dimension latent_dim. We use the states of the encoder as initial_state for the decoder.
What I do not understand is why a dense layer is then added after the LSTM layer and why it is working?
The decoder is supposed to return all the sequence because of return_sequences = True, so how is it possible that adding a dense layer after is working?
I guess I miss something here.
Although the common cases use 2D data (batch,dim) as inputs for dense layers, in newer versions of Keras you can use 3D data (batch,timesteps,dim).
If you don't flatten this 3D data, your Dense layer will behave as if it would be applied to each of the time steps. And you will get outputs like (batch,timesteps,dense_units)
You can check these two little models below and confirm that independently of the time steps, both Dense layers have the same number of parameters, showing its parameters are suited only for the last dimension.
from keras.layers import *
from keras.models import Model
import keras.backend as K
#model with time steps
inp = Input((7,12))
out = Dense(5)(inp)
model = Model(inp,out)
model.summary()
#model without time steps
inp2 = Input((12,))
out2 = Dense(5)(inp2)
model2 = Model(inp2,out2)
model2.summary()
The result will show 65 (12*5 + 5) parameters in both cases.