Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Jump to bottom

I got same MACs and Params between before and after when purning yolov7-tiny #356

Open
Qm-jmz opened this issue Mar 17, 2024 · 1 comment
Open

I got same MACs and Params between before and after when purning yolov7-tiny #356

Qm-jmz opened this issue Mar 17, 2024 · 1 comment

Comments

@Qm-jmz
Copy link

Qm-jmz commented Mar 17, 2024 •
edited

I got same MACs and Params between before and after when purning yolov7-tiny, but yolov7 works well.
this is my code:

import torch

from models.experimental import attempt_load

import torch_pruning as tp

def purned(weights, device):
    # Load model
    model = attempt_load(weights, map_location=device)  # load FP32 model
    print(model)
    
    ################################################################################
    # Pruning
    example_inputs = torch.randn(1, 3, 640, 640).to(device)
    imp = tp.importance.MagnitudeImportance(p=2) # L2 norm pruning
    # imp = tp.importance.BNScaleImportance()
    ignored_layers = []
    from models.yolo import Detect,IDetect
    for m in model.modules():
        if isinstance(m, (Detect,IDetect)):
            ignored_layers.append(m)
    print(ignored_layers)

    iterative_steps = 1 # progressive pruning
    pruner = tp.pruner.MagnitudePruner(
        model,
        example_inputs,
        importance=imp,
        iterative_steps=iterative_steps,
        pruning_ratio=1, # remove 50% channels, ResNet18 = {64, 128, 256, 512} => ResNet18_Half = {32, 64, 128, 256}
        ignored_layers=ignored_layers,
    )
    base_macs, base_nparams = tp.utils.count_ops_and_params(model, example_inputs)
    

    pruner.step()
    pruned_macs, pruned_nparams = tp.utils.count_ops_and_params(model, example_inputs)
    print(model)
    print("Before Pruning: MACs=%f G, #Params=%f G"%(base_macs/1e9, base_nparams/1e9))
    print("After Pruning: MACs=%f G, #Params=%f G"%(pruned_macs/1e9, pruned_nparams/1e9))
    ####################################################################################

purned('best.pt', 'cuda')

what I get:

Fusing layers... 
IDetect.fuse
Model(
  (model): Sequential(
    (0): Conv(
      (conv): Conv2d(3, 32, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1))
      (act): LeakyReLU(negative_slope=0.1, inplace=True)
    )
    (1): Conv(
      (conv): Conv2d(32, 64, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1))
      (act): LeakyReLU(negative_slope=0.1, inplace=True)
    )
    (2): Conv(
      (conv): Conv2d(64, 32, kernel_size=(1, 1), stride=(1, 1))
      (act): LeakyReLU(negative_slope=0.1, inplace=True)
    )
    (3): Conv(
      (conv): Conv2d(64, 32, kernel_size=(1, 1), stride=(1, 1))
      (act): LeakyReLU(negative_slope=0.1, inplace=True)
    )
    (4): Conv(
      (conv): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
      (act): LeakyReLU(negative_slope=0.1, inplace=True)
    )
    (5): Conv(
      (conv): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
      (act): LeakyReLU(negative_slope=0.1, inplace=True)
    )
    (6): Concat()
    (7): Conv(
      (conv): Conv2d(128, 64, kernel_size=(1, 1), stride=(1, 1))
      (act): LeakyReLU(negative_slope=0.1, inplace=True)
    )
    (8): MP(
      (m): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
    )
    (9): Conv(
      (conv): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1))
      (act): LeakyReLU(negative_slope=0.1, inplace=True)
    )
    (10): Conv(
      (conv): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1))
      (act): LeakyReLU(negative_slope=0.1, inplace=True)
    )
    (11): Conv(
      (conv): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
      (act): LeakyReLU(negative_slope=0.1, inplace=True)
    )
    (12): Conv(
      (conv): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
      (act): LeakyReLU(negative_slope=0.1, inplace=True)
    )
    (13): Concat()
    (14): Conv(
      (conv): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1))
      (act): LeakyReLU(negative_slope=0.1, inplace=True)
    )
    (15): MP(
      (m): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
    )
    (16): Conv(
      (conv): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1))
      (act): LeakyReLU(negative_slope=0.1, inplace=True)
    )
    (17): Conv(
      (conv): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1))
      (act): LeakyReLU(negative_slope=0.1, inplace=True)
    )
    (18): Conv(
      (conv): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
      (act): LeakyReLU(negative_slope=0.1, inplace=True)
    )
    (19): Conv(
      (conv): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
      (act): LeakyReLU(negative_slope=0.1, inplace=True)
    )
    (20): Concat()
    (21): Conv(
      (conv): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1))
      (act): LeakyReLU(negative_slope=0.1, inplace=True)
    )
    (22): MP(
      (m): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
    )
    (23): Conv(
      (conv): Conv2d(256, 256, kernel_size=(1, 1), stride=(1, 1))
      (act): LeakyReLU(negative_slope=0.1, inplace=True)
    )
    (24): Conv(
      (conv): Conv2d(256, 256, kernel_size=(1, 1), stride=(1, 1))
      (act): LeakyReLU(negative_slope=0.1, inplace=True)
    )
    (25): Conv(
      (conv): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
      (act): LeakyReLU(negative_slope=0.1, inplace=True)
    )
    (26): Conv(
      (conv): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
      (act): LeakyReLU(negative_slope=0.1, inplace=True)
    )
    (27): Concat()
    (28): Conv(
      (conv): Conv2d(1024, 512, kernel_size=(1, 1), stride=(1, 1))
      (act): LeakyReLU(negative_slope=0.1, inplace=True)
    )
    (29): Conv(
      (conv): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1))
      (act): LeakyReLU(negative_slope=0.1, inplace=True)
    )
    (30): Conv(
      (conv): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1))
      (act): LeakyReLU(negative_slope=0.1, inplace=True)
    )
    (31): SP(
      (m): MaxPool2d(kernel_size=5, stride=1, padding=2, dilation=1, ceil_mode=False)
    )
    (32): SP(
      (m): MaxPool2d(kernel_size=9, stride=1, padding=4, dilation=1, ceil_mode=False)
    )
    (33): SP(
      (m): MaxPool2d(kernel_size=13, stride=1, padding=6, dilation=1, ceil_mode=False)
    )
    (34): Concat()
    (35): Conv(
      (conv): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1))
      (act): LeakyReLU(negative_slope=0.1, inplace=True)
    )
    (36): Concat()
    (37): Conv(
      (conv): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1))
      (act): LeakyReLU(negative_slope=0.1, inplace=True)
    )
    (38): Conv(
      (conv): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1))
      (act): LeakyReLU(negative_slope=0.1, inplace=True)
    )
    (39): Upsample(scale_factor=2.0, mode=nearest)
    (40): Conv(
      (conv): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1))
      (act): LeakyReLU(negative_slope=0.1, inplace=True)
    )
    (41): Concat()
    (42): Conv(
      (conv): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1))
      (act): LeakyReLU(negative_slope=0.1, inplace=True)
    )
    (43): Conv(
      (conv): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1))
      (act): LeakyReLU(negative_slope=0.1, inplace=True)
    )
    (44): Conv(
      (conv): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
      (act): LeakyReLU(negative_slope=0.1, inplace=True)
    )
    (45): Conv(
      (conv): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
      (act): LeakyReLU(negative_slope=0.1, inplace=True)
    )
    (46): Concat()
    (47): Conv(
      (conv): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1))
      (act): LeakyReLU(negative_slope=0.1, inplace=True)
    )
    (48): Conv(
      (conv): Conv2d(128, 64, kernel_size=(1, 1), stride=(1, 1))
      (act): LeakyReLU(negative_slope=0.1, inplace=True)
    )
    (49): Upsample(scale_factor=2.0, mode=nearest)
    (50): Conv(
      (conv): Conv2d(128, 64, kernel_size=(1, 1), stride=(1, 1))
      (act): LeakyReLU(negative_slope=0.1, inplace=True)
    )
    (51): Concat()
    (52): Conv(
      (conv): Conv2d(128, 32, kernel_size=(1, 1), stride=(1, 1))
      (act): LeakyReLU(negative_slope=0.1, inplace=True)
    )
    (53): Conv(
      (conv): Conv2d(128, 32, kernel_size=(1, 1), stride=(1, 1))
      (act): LeakyReLU(negative_slope=0.1, inplace=True)
    )
    (54): Conv(
      (conv): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
      (act): LeakyReLU(negative_slope=0.1, inplace=True)
    )
    (55): Conv(
      (conv): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
      (act): LeakyReLU(negative_slope=0.1, inplace=True)
    )
    (56): Concat()
    (57): Conv(
      (conv): Conv2d(128, 64, kernel_size=(1, 1), stride=(1, 1))
      (act): LeakyReLU(negative_slope=0.1, inplace=True)
    )
    (58): Conv(
      (conv): Conv2d(64, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1))
      (act): LeakyReLU(negative_slope=0.1, inplace=True)
    )
    (59): Concat()
    (60): Conv(
      (conv): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1))
      (act): LeakyReLU(negative_slope=0.1, inplace=True)
    )
    (61): Conv(
      (conv): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1))
      (act): LeakyReLU(negative_slope=0.1, inplace=True)
    )
    (62): Conv(
      (conv): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
      (act): LeakyReLU(negative_slope=0.1, inplace=True)
    )
    (63): Conv(
      (conv): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
      (act): LeakyReLU(negative_slope=0.1, inplace=True)
    )
    (64): Concat()
    (65): Conv(
      (conv): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1))
      (act): LeakyReLU(negative_slope=0.1, inplace=True)
    )
    (66): Conv(
      (conv): Conv2d(128, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1))
      (act): LeakyReLU(negative_slope=0.1, inplace=True)
    )
    (67): Concat()
    (68): Conv(
      (conv): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1))
      (act): LeakyReLU(negative_slope=0.1, inplace=True)
    )
    (69): Conv(
      (conv): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1))
      (act): LeakyReLU(negative_slope=0.1, inplace=True)
    )
    (70): Conv(
      (conv): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
      (act): LeakyReLU(negative_slope=0.1, inplace=True)
    )
    (71): Conv(
      (conv): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
      (act): LeakyReLU(negative_slope=0.1, inplace=True)
    )
    (72): Concat()
    (73): Conv(
      (conv): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1))
      (act): LeakyReLU(negative_slope=0.1, inplace=True)
    )
    (74): Conv(
      (conv): Conv2d(64, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
      (act): LeakyReLU(negative_slope=0.1, inplace=True)
    )
    (75): Conv(
      (conv): Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
      (act): LeakyReLU(negative_slope=0.1, inplace=True)
    )
    (76): Conv(
      (conv): Conv2d(256, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
      (act): LeakyReLU(negative_slope=0.1, inplace=True)
    )
    (77): IDetect(
      (m): ModuleList(
        (0): Conv2d(128, 48, kernel_size=(1, 1), stride=(1, 1))
        (1): Conv2d(256, 48, kernel_size=(1, 1), stride=(1, 1))
        (2): Conv2d(512, 48, kernel_size=(1, 1), stride=(1, 1))
      )
      (ia): ModuleList(
        (0): ImplicitA()
        (1): ImplicitA()
        (2): ImplicitA()
      )
      (im): ModuleList(
        (0): ImplicitM()
        (1): ImplicitM()
        (2): ImplicitM()
      )
    )
  )
)
[IDetect(
  (m): ModuleList(
    (0): Conv2d(128, 48, kernel_size=(1, 1), stride=(1, 1))
    (1): Conv2d(256, 48, kernel_size=(1, 1), stride=(1, 1))
    (2): Conv2d(512, 48, kernel_size=(1, 1), stride=(1, 1))
  )
  (ia): ModuleList(
    (0): ImplicitA()
    (1): ImplicitA()
    (2): ImplicitA()
  )
  (im): ModuleList(
    (0): ImplicitM()
    (1): ImplicitM()
    (2): ImplicitM()
  )
)]
C:\Users\AppData\Roaming\Python\Python310\site-packages\torch_pruning\dependency.py:667: UserWarning: Unwrapped parameters detected: ['model.77.ia.0.implicit', 'model.77.ia.2.implicit', 'model.77.im.2.implicit', 'model.77.im.0.implicit', 'model.77.ia.1.implicit', 'model.77.im.1.implicit'].
 Torch-Pruning will prune the last non-singleton dimension of these parameters. If you wish to change this behavior, please provide an unwrapped_parameters argument.
  warnings.warn(warning_str)
Model(
  (model): Sequential(
    (0): Conv(
      (conv): Conv2d(3, 32, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1))
      (act): LeakyReLU(negative_slope=0.1, inplace=True)
    )
    (1): Conv(
      (conv): Conv2d(32, 64, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1))
      (act): LeakyReLU(negative_slope=0.1, inplace=True)
    )
    (2): Conv(
      (conv): Conv2d(64, 32, kernel_size=(1, 1), stride=(1, 1))
      (act): LeakyReLU(negative_slope=0.1, inplace=True)
    )
    (3): Conv(
      (conv): Conv2d(64, 32, kernel_size=(1, 1), stride=(1, 1))
      (act): LeakyReLU(negative_slope=0.1, inplace=True)
    )
    (4): Conv(
      (conv): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
      (act): LeakyReLU(negative_slope=0.1, inplace=True)
    )
    (5): Conv(
      (conv): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
      (act): LeakyReLU(negative_slope=0.1, inplace=True)
    )
    (6): Concat()
    (7): Conv(
      (conv): Conv2d(128, 64, kernel_size=(1, 1), stride=(1, 1))
      (act): LeakyReLU(negative_slope=0.1, inplace=True)
    )
    (8): MP(
      (m): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
    )
    (9): Conv(
      (conv): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1))
      (act): LeakyReLU(negative_slope=0.1, inplace=True)
    )
    (10): Conv(
      (conv): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1))
      (act): LeakyReLU(negative_slope=0.1, inplace=True)
    )
    (11): Conv(
      (conv): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
      (act): LeakyReLU(negative_slope=0.1, inplace=True)
    )
    (12): Conv(
      (conv): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
      (act): LeakyReLU(negative_slope=0.1, inplace=True)
    )
    (13): Concat()
    (14): Conv(
      (conv): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1))
      (act): LeakyReLU(negative_slope=0.1, inplace=True)
    )
    (15): MP(
      (m): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
    )
    (16): Conv(
      (conv): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1))
      (act): LeakyReLU(negative_slope=0.1, inplace=True)
    )
    (17): Conv(
      (conv): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1))
      (act): LeakyReLU(negative_slope=0.1, inplace=True)
    )
    (18): Conv(
      (conv): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
      (act): LeakyReLU(negative_slope=0.1, inplace=True)
    )
    (19): Conv(
      (conv): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
      (act): LeakyReLU(negative_slope=0.1, inplace=True)
    )
    (20): Concat()
    (21): Conv(
      (conv): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1))
      (act): LeakyReLU(negative_slope=0.1, inplace=True)
    )
    (22): MP(
      (m): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
    )
    (23): Conv(
      (conv): Conv2d(256, 256, kernel_size=(1, 1), stride=(1, 1))
      (act): LeakyReLU(negative_slope=0.1, inplace=True)
    )
    (24): Conv(
      (conv): Conv2d(256, 256, kernel_size=(1, 1), stride=(1, 1))
      (act): LeakyReLU(negative_slope=0.1, inplace=True)
    )
    (25): Conv(
      (conv): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
      (act): LeakyReLU(negative_slope=0.1, inplace=True)
    )
    (26): Conv(
      (conv): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
      (act): LeakyReLU(negative_slope=0.1, inplace=True)
    )
    (27): Concat()
    (28): Conv(
      (conv): Conv2d(1024, 512, kernel_size=(1, 1), stride=(1, 1))
      (act): LeakyReLU(negative_slope=0.1, inplace=True)
    )
    (29): Conv(
      (conv): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1))
      (act): LeakyReLU(negative_slope=0.1, inplace=True)
    )
    (30): Conv(
      (conv): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1))
      (act): LeakyReLU(negative_slope=0.1, inplace=True)
    )
    (31): SP(
      (m): MaxPool2d(kernel_size=5, stride=1, padding=2, dilation=1, ceil_mode=False)
    )
    (32): SP(
      (m): MaxPool2d(kernel_size=9, stride=1, padding=4, dilation=1, ceil_mode=False)
    )
    (33): SP(
      (m): MaxPool2d(kernel_size=13, stride=1, padding=6, dilation=1, ceil_mode=False)
    )
    (34): Concat()
    (35): Conv(
      (conv): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1))
      (act): LeakyReLU(negative_slope=0.1, inplace=True)
    )
    (36): Concat()
    (37): Conv(
      (conv): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1))
      (act): LeakyReLU(negative_slope=0.1, inplace=True)
    )
    (38): Conv(
      (conv): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1))
      (act): LeakyReLU(negative_slope=0.1, inplace=True)
    )
    (39): Upsample(scale_factor=2.0, mode=nearest)
    (40): Conv(
      (conv): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1))
      (act): LeakyReLU(negative_slope=0.1, inplace=True)
    )
    (41): Concat()
    (42): Conv(
      (conv): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1))
      (act): LeakyReLU(negative_slope=0.1, inplace=True)
    )
    (43): Conv(
      (conv): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1))
      (act): LeakyReLU(negative_slope=0.1, inplace=True)
    )
    (44): Conv(
      (conv): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
      (act): LeakyReLU(negative_slope=0.1, inplace=True)
    )
    (45): Conv(
      (conv): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
      (act): LeakyReLU(negative_slope=0.1, inplace=True)
    )
    (46): Concat()
    (47): Conv(
      (conv): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1))
      (act): LeakyReLU(negative_slope=0.1, inplace=True)
    )
    (48): Conv(
      (conv): Conv2d(128, 64, kernel_size=(1, 1), stride=(1, 1))
      (act): LeakyReLU(negative_slope=0.1, inplace=True)
    )
    (49): Upsample(scale_factor=2.0, mode=nearest)
    (50): Conv(
      (conv): Conv2d(128, 64, kernel_size=(1, 1), stride=(1, 1))
      (act): LeakyReLU(negative_slope=0.1, inplace=True)
    )
    (51): Concat()
    (52): Conv(
      (conv): Conv2d(128, 32, kernel_size=(1, 1), stride=(1, 1))
      (act): LeakyReLU(negative_slope=0.1, inplace=True)
    )
    (53): Conv(
      (conv): Conv2d(128, 32, kernel_size=(1, 1), stride=(1, 1))
      (act): LeakyReLU(negative_slope=0.1, inplace=True)
    )
    (54): Conv(
      (conv): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
      (act): LeakyReLU(negative_slope=0.1, inplace=True)
    )
    (55): Conv(
      (conv): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
      (act): LeakyReLU(negative_slope=0.1, inplace=True)
    )
    (56): Concat()
    (57): Conv(
      (conv): Conv2d(128, 64, kernel_size=(1, 1), stride=(1, 1))
      (act): LeakyReLU(negative_slope=0.1, inplace=True)
    )
    (58): Conv(
      (conv): Conv2d(64, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1))
      (act): LeakyReLU(negative_slope=0.1, inplace=True)
    )
    (59): Concat()
    (60): Conv(
      (conv): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1))
      (act): LeakyReLU(negative_slope=0.1, inplace=True)
    )
    (61): Conv(
      (conv): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1))
      (act): LeakyReLU(negative_slope=0.1, inplace=True)
    )
    (62): Conv(
      (conv): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
      (act): LeakyReLU(negative_slope=0.1, inplace=True)
    )
    (63): Conv(
      (conv): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
      (act): LeakyReLU(negative_slope=0.1, inplace=True)
    )
    (64): Concat()
    (65): Conv(
      (conv): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1))
      (act): LeakyReLU(negative_slope=0.1, inplace=True)
    )
    (66): Conv(
      (conv): Conv2d(128, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1))
      (act): LeakyReLU(negative_slope=0.1, inplace=True)
    )
    (67): Concat()
    (68): Conv(
      (conv): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1))
      (act): LeakyReLU(negative_slope=0.1, inplace=True)
    )
    (69): Conv(
      (conv): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1))
      (act): LeakyReLU(negative_slope=0.1, inplace=True)
    )
    (70): Conv(
      (conv): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
      (act): LeakyReLU(negative_slope=0.1, inplace=True)
    )
    (71): Conv(
      (conv): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
      (act): LeakyReLU(negative_slope=0.1, inplace=True)
    )
    (72): Concat()
    (73): Conv(
      (conv): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1))
      (act): LeakyReLU(negative_slope=0.1, inplace=True)
    )
    (74): Conv(
      (conv): Conv2d(64, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
      (act): LeakyReLU(negative_slope=0.1, inplace=True)
    )
    (75): Conv(
      (conv): Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
      (act): LeakyReLU(negative_slope=0.1, inplace=True)
    )
    (76): Conv(
      (conv): Conv2d(256, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
      (act): LeakyReLU(negative_slope=0.1, inplace=True)
    )
    (77): IDetect(
      (m): ModuleList(
        (0): Conv2d(128, 48, kernel_size=(1, 1), stride=(1, 1))
        (1): Conv2d(256, 48, kernel_size=(1, 1), stride=(1, 1))
        (2): Conv2d(512, 48, kernel_size=(1, 1), stride=(1, 1))
      )
      (ia): ModuleList(
        (0): ImplicitA()
        (1): ImplicitA()
        (2): ImplicitA()
      )
      (im): ModuleList(
        (0): ImplicitM()
        (1): ImplicitM()
        (2): ImplicitM()
      )
    )
  )
)
Before Pruning: MACs=6.597421 G, #Params=0.006035 G
After Pruning: MACs=6.597421 G, #Params=0.006035 G
@Qm-jmz Qm-jmz changed the title I got same MACs and Params between before and after when purning yolov7 I got same MACs and Params between before and after when purning yolov7-tiny Mar 17, 2024
@0doing0
Copy link

0doing0 commented Mar 23, 2024

Did you set the "requires_grad" of the modules that you want to prune to True?

Sign up for free to join this conversation on GitHub. Already have an account? Sign in to comment
Assignees
No one assigned
Labels
None yet
Projects
None yet
Milestone
No milestone
Development

No branches or pull requests
2 participants
@0doing0 @Qm-jmz